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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Water</journal-id>
<journal-title-group>
<journal-title>Frontiers in Water</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Water</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2624-9375</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/frwa.2025.1622293</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Assessing the contribution of climate change on tropical cyclones related to loss and damage in southern Africa: a case study of tropical cyclones in southern Malawi</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Joshua</surname><given-names>Miriam Dalitso Kalanda</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/3184207"/>
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</contrib>
<contrib contrib-type="author">
<name><surname>Kasei</surname><given-names>Raymond Abudu</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/3050025"/>
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</contrib>
<contrib contrib-type="author">
<name><surname>Wamukoya</surname><given-names>George</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<aff id="aff1"><label>1</label><institution>School of Natural and Applied Sciences, University of Malawi</institution>, <city>Zomba</city>, <country country="mw">Malawi</country></aff>
<aff id="aff2"><label>2</label><institution>Malcolm Baldrige School of Business, Post University</institution>, <city>Waterbury</city>, <state>CT</state>, <country country="us">United States</country></aff>
<aff id="aff3"><label>3</label><institution>School of Engineering, University for Development Studies</institution>, <city>Tamale</city>, <country country="gh">Ghana</country></aff>
<aff id="aff4"><label>4</label><institution>African Group of Negotiators Experts Support (AGNES)</institution>, <city>Nairobi</city>, <country country="ke">Kenya</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Miriam Dalitso Kalanda Joshua, <email xlink:href="mailto:madalitsojoshua@yahoo.com">madalitsojoshua@yahoo.com</email>; <email xlink:href="mailto:mjoshua@unima.ac.mw">mjoshua@unima.ac.mw</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-01-12">
<day>12</day>
<month>01</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>7</volume>
<elocation-id>1622293</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>06</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>13</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>11</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2026 Joshua, Kasei and Wamukoya.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Joshua, Kasei and Wamukoya</copyright-holder>
<license>
<ali:license_ref start_date="2026-01-12">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<p>This study synthesizes peer-reviewed literature and government reports to examine the relationship between climate change and the frequency, occurrence, and intensity of tropical cyclones in southern Malawi. The research employs a mixed-methods approach, combining spatial mapping, literature synthesis, and trend analysis of cyclone data from the past three decades. It also analyzes rainfall data from nine meteorological stations using the Standardized Precipitation Index (SPI) and conducts stakeholder interviews across four districts. Using literature review of peer-reviewed literature and government documents, this study assessed the link between climate change and the occurrence, frequency, and magnitude of tropical cyclones that lead to Loss and damage in several areas, including various types of physical infrastructure, agriculture, and food security in Africa. The study focuses on Southern Africa, using Malawi as a case study. It examines the occurrence and frequency of tropical cyclones in the Southern region over the past 30&#x202F;years. Specifically, the study aims to: (i) Analyze trends of tropical cyclones and related temperature and extreme rainfall events, including floods, for the past three decades, and (ii) Map areas affected by Tropical cyclones and related extreme rainfall events over the past 30&#x202F;years. Findings reveal an increasing trend in tropical cyclone occurrences since the 2000s, with particularly intense events such as those in 2015, 2019, and 2023 coinciding with La Ni&#x00F1;a conditions. Statistical analysis using Mann-Kendall trend tests and Pearson correlations confirms significant upward trends in both cyclone frequency (Tau&#x202F;=&#x202F;0.29, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05) and rainfall anomalies (<italic>r</italic>&#x202F;=&#x202F;0.51, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.01). The study results show an increasing occurrence of Tropical cyclones from the 2000s. These findings demonstrate correlations between increased cyclone activity and climate change indicators, consistent with established attribution studies but requiring additional multivariable climate modeling for definitive causal attribution, reinforcing the need to prioritize agricultural resilience within the Loss and Damage framework under the UNFCCC. The increasing trends in cyclone frequency and intensity show correlations with climate change patterns and align with established climate projections. However, definitively establishing direct causal attribution requires comprehensive climate modeling, which is beyond the scope of this study. These observed trends are consistent with regional attribution studies, suggesting that management of losses and damages in agriculture deserves special attention within the Loss and Damage framework.</p>
</abstract>
<kwd-group>
<kwd>loss and damage fund</kwd>
<kwd>tropical cyclones</kwd>
<kwd>attribution</kwd>
<kwd>climate change</kwd>
<kwd>frequency</kwd>
<kwd>intensity</kwd>
<kwd>Africa</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was not received for this work and/or its publication.</funding-statement>
</funding-group>
<counts>
<fig-count count="24"/>
<table-count count="8"/>
<equation-count count="1"/>
<ref-count count="80"/>
<page-count count="29"/>
<word-count count="14666"/>
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<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Water and Climate</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<sec id="sec2">
<label>1.1</label>
<title>Background</title>
<p>Climate change has emerged as a paramount global challenge, with far-reaching impacts on economies, livelihoods, and biodiversity. While acknowledging the multifaceted nature of climate risks in Southern Africa, this study focuses specifically on tropical cyclones due to their growing intensity, rapid onset, and acute impacts on food systems and infrastructure, areas highly relevant to Loss and Damage mechanisms. The increasing frequency and intensity of extreme weather events are significantly hindering efforts to achieve the Sustainable Development Goals (SDGs) and exacerbating vulnerabilities in many developing countries (<xref ref-type="bibr" rid="ref23">Intergovernmental Panel on Climate Change, 2022</xref>). Among these extreme events, tropical cyclones pose a particular threat to southern Africa, with their destructive potential amplified by climate change.</p>
<p>The concept of loss and damage has gained prominence in international climate negotiations, particularly for vulnerable developing countries. It refers to the impacts of climate change that cannot be avoided through mitigation and adaptation efforts (<xref ref-type="bibr" rid="ref61">UNFCCC, 2013a</xref>,<xref ref-type="bibr" rid="ref62">b</xref>). The international political response to climate change began with the adoption of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 which recognizes the fundamental priorities of protecting livelihoods and economies (Article 2) and addressing the particular vulnerabilities of developing countries to the adverse impacts of climate change (Article 3) when planning and implementing response measures (<xref ref-type="bibr" rid="ref60">UNFCCC, 1992</xref>). The Paris Agreement of 2015 explicitly acknowledges the importance of &#x201C;averting, minimizing and addressing loss and damage associated with the adverse effects of climate change&#x201D; (<xref ref-type="bibr" rid="ref63">UNFCCC, 2015</xref>). This has led to increased focus on developing mechanisms to support vulnerable countries in managing the impacts of extreme events such as tropical cyclones.</p>
<p>Tropical cyclones (TCs) are one of the most devastating natural hazards, characterized by intense winds, heavy rainfall, and storm surges that cause widespread destruction to infrastructure, ecosystems, lives, and human livelihoods. Globally, regions such as the North Atlantic, Western Pacific, Indian Ocean, and South Pacific are particularly prone to these extreme weather events, with the intensity and frequency of cyclones exhibiting significant variability over time (<xref ref-type="bibr" rid="ref31">Kossin et al., 2020a</xref>,<xref ref-type="bibr" rid="ref33">b</xref>). The increasing visibility of climate change&#x2019;s impacts on weather patterns across the world has intensified scientific efforts to attribute observed changes in tropical cyclone behavior to anthropogenic influences.</p>
<p>The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) highlights that while there is limited evidence for a clear trend in the overall frequency of tropical cyclones globally, there is stronger evidence indicating an increase in the proportion of intense cyclones (Category 3 and above) in some ocean basins, notably the North Atlantic and Western Pacific (<xref ref-type="bibr" rid="ref21">Intergovernmental Panel on Climate Change, 2013</xref>). These findings suggest that climate change may be influencing the intensity and distribution of TCs, with regional disparities driven by local sea surface temperature (SST) anomalies, atmospheric conditions, and climate variability.</p>
<p>Recent studies have further documented complex interactions between climate change and tropical cyclone activity showing a marked increase in the frequency and intensity of tropical cyclones (TCs) affecting southern Africa. The Intergovernmental Panel on Climate Change (IPCC) projects that, because of global warming, the frequency and intensity of heavy rainfall events will increase for most of tropical Africa, while parts of northern and southern Africa will become drier (<xref ref-type="bibr" rid="ref22">Intergovernmental Panel on Climate Change, 2021</xref>). Further, <xref ref-type="bibr" rid="ref50">Reason et al. (2018)</xref> reported that SSTs in the South Indian Ocean have increased over the past decades, potentially fostering conditions conducive to cyclone development. However, the attribution of specific cyclone events or trends remains challenging due to limited historical data, regional climate variability, and the complex interplay of oceanic and atmospheric factors (<xref ref-type="bibr" rid="ref34">Leroux et al., 2017</xref>). This is likely to result in significantly increased risk of flooding and associated damages.</p>
<p>For instance, the case of Malawi and Southern Africa in general exemplifies the broader regional challenges faced by cyclone-prone areas worldwide. While historically less affected by tropical cyclones compared to regions like the Caribbean or Southeast Asia, Southern Africa has experienced an increasing incidence of intense weather systems linked to climate variability and change (<xref ref-type="bibr" rid="ref52">Reason and Van Heerden, 2012</xref>). Notable extreme rainfall events, such as Cyclone Idai in 2019 and Cyclone Freddy in 2023, have underscored the urgent need to understand how climate change influences cyclone characteristics and impacts in this region.</p>
<p>Globally, the attribution of specific cyclone events to climate change has advanced through event attribution science, which assesses the extent to which human influence has altered the likelihood or severity of such events (<xref ref-type="bibr" rid="ref55">Stott et al., 2016</xref>). For example, <xref ref-type="bibr" rid="ref67">Van Oldenborgh et al. (2017)</xref> attributed the increased probability of extreme rainfall during Cyclone Idai to anthropogenic climate change. Similar approaches are being increasingly applied to understand regional variations and inform adaptation strategies. <xref ref-type="bibr" rid="ref57">Titley et al. (2016)</xref> have demonstrated that climate change has increased the likelihood and intensity of rainfall associated with tropical cyclones impacting Madagascar, Mozambique, and Malawi. This finding highlights the direct connection between human-induced climate change and the growing losses and damages in the region.</p>
<p>This paper aims to contribute to this growing body of knowledge by investigating the attribution of tropical cyclones to climate change in Southern Africa, with a specific focus on Malawi. While the case study is region-specific, its findings have broader implications for understanding how climate change may be affecting cyclone activity in tropical and subtropical regions worldwide. Addressing this issue is critical not only for regional disaster preparedness and resilience planning but also for informing global climate policy and adaptation efforts.</p>
</sec>
<sec id="sec3">
<label>1.2</label>
<title>Understanding loss and damage in the context of UNFCCC</title>
<p>The adverse impacts of climate change, ranging from extreme weather events to slow-onset changes, have become increasingly evident across the globe. As countries strive to mitigate and adapt to these impacts, the concept of loss and damage has emerged as a critical component of climate change discourse. Although it is not officially defined under the UNFCCC, loss and damage refer to the irreversible and non-compensable harm caused by climate change, which exceeds the capacity of communities, ecosystems, and nations to adapt and recover (<xref ref-type="bibr" rid="ref56">Telesetsky, 2021</xref>). <xref ref-type="bibr" rid="ref23">Intergovernmental Panel on Climate Change (2022)</xref> further defines loss and damage as the &#x201C;harm from (observed) impacts and (projected) risks&#x201D; (p. 2914) of anthropogenic climate change. This harm includes both economic and non-economic impacts resulting from extreme weather events (rapid-onset events) and slow-onset events (<xref ref-type="bibr" rid="ref49">Qi et al., 2023</xref>).</p>
<p>Within the United Nations Framework Convention on Climate Change (UNFCCC), loss and damage have gained recognition as a distinct issue that necessitates targeted action. The evolution of loss and damage in UNFCCC negotiations has seen significant milestones, including the establishment and operationalization of the Warsaw International Mechanism for Loss and Damage (WIM), the Santiago Network on loss and damage (Santiago Network), and the Fund for Responding to Loss and Damage (FRLD) (<xref ref-type="bibr" rid="ref64">UNFCCC, 2023</xref>). The recognition of loss and damage in the Paris Agreement further emphasizes its significance on the global climate change agenda (<xref ref-type="bibr" rid="ref63">UNFCCC, 2015</xref>).</p>
<p>It is widely recognized that national or regional assessments play a crucial role in comprehending the specific vulnerabilities, risks, and impacts associated with loss and damage within a given geographic context. These assessments provide valuable insights into the localized manifestations of loss and damage, guiding policymakers and practitioners in developing effective adaptation and mitigation strategies. By integrating these assessments into national or regional policies and planning processes, including national adaptation planning (NAPs) and nationally determined contributions (NDCs), countries and regions can better understand the challenges they face and allocate resources accordingly.</p>
</sec>
<sec id="sec4">
<label>1.3</label>
<title>Research problem statement and justification</title>
<p>The Fund for Responding to Loss and Damage (FRLD) is part of the UNFCCC&#x2019;s financial mechanism to assist countries that are particularly vulnerable to the adverse effects of climate change in responding to economic and non-economic loss and damage associated with extreme weather events and slow-onset events. Attribution science plays a crucial role in understanding the extent to which human-caused climate change is responsible for the losses and damages. Attribution is often associated with responsibility and responsibility/liability, and in establishing a clear causal relationship (<xref ref-type="bibr" rid="ref27">Knutson et al., 2019</xref>; <xref ref-type="bibr" rid="ref28">Knutson et al., 2020</xref>). For example, attribution studies can justify the need for enhanced financial investments, such as climate risk insurance, climate risk pools, and catastrophe bonds, to address the escalating costs associated with more intense and destructive extreme weather events.</p>
<p>However, significant research gaps remain, particularly regarding regional variability in climate change, impacts on tropical cyclone behavior, and the uncertainty surrounding future storm frequency and intensity under different emission scenarios. Furthermore, research on the attribution of climate change to tropical cyclones has a long history globally, but in Southern Africa, it is still emerging, with several gaps remaining that need to be addressed to improve understanding and predictive capabilities. There is limited high-resolution data and Regional Climate Model (RCM) data. In this regard, most attribution studies rely on global climate models that lack the spatial resolution necessary to accurately simulate the genesis, track, and intensity of tropical cyclones in the Southern African region (<xref ref-type="bibr" rid="ref51">Reason and Rouault, 2020</xref>).</p>
<p>Studies show that there are insufficient or inconsistent long-term historical records of tropical cyclones affecting Southern Africa, which hinders robust attribution analysis (<xref ref-type="bibr" rid="ref52">Reason and Van Heerden, 2012</xref>). This gap hinders robust attribution analysis. Hence, improved reconstruction of past cyclone activity and improved historical datasets are needed for trend analysis and attribution studies. Likewise, there is a limited understanding of the climate change signal in cyclone intensity and frequency. There is an ongoing debate over how climate change affects the frequency and intensity of tropical cyclones in the Southern Hemisphere, with some studies indicating an increase in intensity but no clear trend in frequency (<xref ref-type="bibr" rid="ref34">Leroux et al., 2017</xref>).</p>
<p>Attribution of specific extreme events (e.g., Cyclone Idai in 2019 and Tropical Cyclone Freddy in 2023) to climate change remains complex (<xref ref-type="bibr" rid="ref67">Van Oldenborgh et al., 2017</xref>), underscoring the need for event-based attribution studies that quantify the extent to which climate change has influenced the probability or severity of individual cyclones. Similarly, the impact of Sea Surface Temperature (SST) changes is underexplored. Changes in SSTs are known to influence cyclone development, and regional SST variability in the Indian Ocean and its relationship with cyclone activity in Southern Africa is not fully understood (<xref ref-type="bibr" rid="ref7">Elsner et al., 2008</xref>; <xref ref-type="bibr" rid="ref50">Reason et al., 2018</xref>). Further research is needed to establish a connection between SST anomalies and cyclone genesis and intensification in the context of climate change.</p>
<p>Additionally, the Lilongwe Declaration on Climate Change 2024 (<xref ref-type="bibr" rid="ref65">United Nations, 2024a</xref>,<xref ref-type="bibr" rid="ref66">b</xref>) calls for international support and legal protection for people displaced by climate change, particularly women and children, recognizing that losses and damages due to tropical cyclones in Southern Africa are growing over time. This study contends that addressing these gaps will enhance the reliability of climate-related disaster financing and ensure more resilient and evidence-based responses to climate-induced tropical cyclone hazards. For example, the Lilongwe Declaration on Climate Change 2024 calls for international support and legal protection for people displaced by climate change, particularly women and children. The Lilongwe Declaration of 2024 emphasizes that losses and damages due to tropical cyclones<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> in Southern Africa are increasing over time, and some of these losses, such as housing infrastructure, lead to the displacement of households. Therefore, this study aims to inform financing mechanisms of the losses and damages caused by Tropical Cyclones using Malawi as a case study.</p>
</sec>
<sec id="sec5">
<label>1.4</label>
<title>Study objectives</title>
<p>The primary objective of this study is to examine the relationship between tropical cyclones and climate change in Southern Africa, with a particular focus on Tropical Cyclones in southern Malawi.</p>
<p>Specifically, the study aims to:</p><list list-type="roman-lower">
<list-item>
<p>Analyze the temporal trends in the frequency and intensity of tropical cyclones in southern Malawi from 1990 to 2023 in the context of climate change.</p>
</list-item>
<list-item>
<p>Map areas affected by tropical cyclones and related extreme rainfall events over the past 30&#x202F;years.</p>
</list-item>
<list-item>
<p>Analyze the potential impact of climate change on the frequency and intensity of tropical cyclones in the region.</p>
</list-item>
<list-item>
<p>Integrate qualitative data from stakeholder interviews and community perspectives using NVivo, and juxtapose these with quantitative cyclone and rainfall data to guide policy recommendations for climate adaptation and risk management.</p>
</list-item>
</list>
<p>The study adopts a mixed-methods approach, combining spatial mapping with literature synthesis and trend analysis of cyclone data from the past three decades. GIS mapping and manual reports have been utilized to identify cyclone-affected regions, while statistical analysis, including Mann-Kendall trend tests and correlation assessments, strengthens the quantitative analysis.</p>
<p>While acknowledging the multifaceted nature of climate risks in Southern Africa, this study focuses specifically on tropical cyclones due to their growing intensity, rapid onset, and acute impacts on food systems and infrastructure, areas highly relevant to Loss and Damage mechanisms.</p>
<p>The study excludes other types of natural hazards and disasters, such as droughts or floods, which may impact different sectors, including agriculture and food security in the region, but are not directly related to tropical cyclones. This work aims to provide a better understanding of the losses and damages caused by tropical cyclones in Southern Africa.</p>
</sec>
</sec>
<sec sec-type="materials|methods" id="sec6">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec7">
<label>2.1</label>
<title>Study sites and research approach</title>
<p>Malawi is a landlocked country located in southeastern Africa, lying between approximately 9&#x00B0; and 17&#x00B0; South latitude and 33&#x00B0; and 35&#x00B0; East longitude, and is bordered by Tanzania, Zambia, and Mozambique. The country lies within the Great Rift Valley, a significant tectonic and geographical feature that stretches from the Middle East down through East Africa (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Its terrain is largely characterized by highlands and plateaus with elevations ranging from around 300&#x202F;m above sea level in the lake area to over 2,300&#x202F;m in the highland regions. This geographical positioning places Malawi within the subtropical climate zone, with a warm and humid climate influenced by its proximity to the Indian Ocean and the Indian monsoon system. This position makes the country vulnerable to seasonal weather patterns, including rainfall variability and the impacts of tropical cyclones originating from the Indian Ocean, particularly between November and April (<xref ref-type="bibr" rid="ref15">Government of Malawi, 2017</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Map of Malawi and its location in Africa.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map of Africa highlighting water bodies and regional boundaries. The inset zooms into the area around Lake Malawi, bordered by Tanzania, Zambia, Mozambique, and Malawi. Major regions are labeled, and a legend indicates international and regional boundaries.</alt-text>
</graphic>
</fig>
<p>Malawi is highly vulnerable to several hydro-meteorological hazards, including tropical cyclones and floods, especially in the Southern region. In southern Malawi, the most recent flood events were directly triggered or exacerbated by tropical cyclones, reinforcing the rationale for focusing on these events. The country&#x2019;s high level of vulnerability is largely connected with several specific geo-climatic factors, including: &#x201C;(i) the influence of the El Ni&#x00F1;o and La Ni&#x00F1;a phenomena on climate variability; (ii) the variability in the water levels of the country&#x2019;s three major lakes (Malawi, Chiuta, and Chilwa) and the broader hydrological network, due to variations in rainfall and other factors; and (iii) the location of Malawi along a tectonically active boundary between two major African plates within the great East African Rift System, which creates vulnerability to earthquakes and landslides (<xref ref-type="bibr" rid="ref16">Government of Malawi, 2019</xref>). The Intergovernmental Panel on Climate Change identifies Malawi as a country at high risk of the adverse effects of climate change (<xref ref-type="bibr" rid="ref22">Intergovernmental Panel on Climate Change, 2021</xref>).</p>
<p>The study focuses on Blantyre, Chikwawa, Zomba, Phalombe, and Mulanje as case study districts that were severely affected by Tropical Cyclone Freddy in March 2023, as well as by previous cyclones, including Idai and Anna. Cyclone Freddy affected over 2.2 million people, displaced more than 143,000 households, and caused 679 fatalities in Malawi (<xref ref-type="bibr" rid="ref9004">Government of Malawi, 2023</xref>). Over the past five decades, the country has experienced more than 19 major flooding incidents, along with warming temperatures and high levels of variation in average annual rainfall (<xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>, <xref ref-type="bibr" rid="ref16">2019</xref>).</p>
<p>Over the past five decades, the country has experienced more than 19 major flooding incidents, along with warming temperatures (<xref ref-type="bibr" rid="ref9001">Bahadur et al., 2013</xref>; <xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>, <xref ref-type="bibr" rid="ref9001">2016</xref>, <xref ref-type="bibr" rid="ref9003">2018/2019</xref>, <xref ref-type="bibr" rid="ref9005">2022</xref>, <xref ref-type="bibr" rid="ref9004">2023</xref>), and a high level of variation in average annual rainfall. In southern Malawi, the most recent flood events were directly triggered or exacerbated by tropical cyclones, reinforcing the rationale for focusing on these events. Cyclone Freddy in 2023, for instance, affected over 2.2 million people, displaced more than 143,000 households, and caused 679 fatalities in Malawi, underscoring the acute vulnerability of the region&#x2019;s infrastructure and communities (<xref ref-type="bibr" rid="ref18">Government of Malawi, 2023b</xref>). For example, Malawi experienced very high rainfall levels in 1989, 1997, 2015, 2019, and 2023, but 1992, 2005, 2008, and 2016 were notably dry. Of all the weather-related shocks to which Malawi is susceptible, droughts and floods are the most common and have had the most significant impact on the country&#x2019;s economy, the lives, and livelihoods of its people, and its infrastructure (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Frequency of shocks by district (2000&#x2013;2013). Source: adopted from <xref ref-type="bibr" rid="ref72">World Bank (2017)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Stacked bar chart displaying the frequency of various natural disasters across different districts. Each bar represents a district, with segments for earthquakes, hailstorms, cyclones, strong winds, heavy rainstorms, droughts, and floods. Karonga has the highest total, particularly floods and heavy rainstorms. Chitipa is notable for earthquakes.</alt-text>
</graphic>
</fig>
<p>While both droughts and floods are the most common and have significant impacts on the country, floods are the primary focus of this study because they occur more frequently (<xref ref-type="bibr" rid="ref20">Holmes et al., 2017</xref>). According to the <xref ref-type="bibr" rid="ref15">Government of Malawi (2017)</xref>, floods have become the most frequent hazard, as well as primary accounting for over 70% of climate-related disasters. Furthermore, they show an increasing trend since the 1970s (<xref ref-type="bibr" rid="ref12">Government of Malawi, 2011</xref>; <xref ref-type="bibr" rid="ref48">Pourazar, 2017</xref>) (<xref ref-type="fig" rid="fig3">Figure 3</xref>), and the number of affected districts has increased over the past two decades.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Occurrence of droughts and floods, 1970&#x2013;2010. Source of data: <xref ref-type="bibr" rid="ref1">Action Aid (2006)</xref> and <xref ref-type="bibr" rid="ref5">Centre for Research on the Epidemiology of Disasters (CRED) (2012)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Three bar charts display occurrences of weather events over decades from 1970 to 2010. The first chart shows total occurrences increasing, with a trend line R&#x00B2; of 0.9706. The second chart depicts flood occurrences rising, with a trend line R&#x00B2; of 0.9323. The third chart illustrates drought occurrences peaking in the 1990s, with a polynomial trend line R&#x00B2; of 0.8615.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec8">
<label>2.2</label>
<title>Data collection methods</title>
<p>The study applies a mixed (qualitative and quantitative) approach, combining spatial mapping, literature synthesis, stakeholder interviews, and trend analysis of cyclone data from the past three decades. Data were collected through primary interviews, direct observations, literature reviews, and both qualitative and quantitative historical cyclone analyses. Spatial mapping was conducted using GIS tools, and rainfall data were systematically retrieved from the Climate Change and Meteorological Department.</p>
<p>Data collection methods included:</p><list list-type="bullet">
<list-item>
<p>Analysis of climate data obtained from weather stations located in the study sites, providing trends of tropical cyclones and associated extreme rainfall events. Historical data on the occurrence, frequency, intensity, and trajectory of tropical cyclones were gathered and analyzed to identify trends, patterns, and areas most affected (<xref ref-type="bibr" rid="ref17">Government of Malawi, 2023a</xref>).</p>
</list-item>
<list-item>
<p>Ground-truthing through key informant interviews with purposively selected traditional leaders and government officers responsible for disaster risk management in districts, including Chikwawa, Phalombe, Mulanje, and Chiradzulu (<xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>, <xref ref-type="bibr" rid="ref16">2019</xref>, <xref ref-type="bibr" rid="ref17">2023a</xref>). The study employed observations to note changes that had taken place in the study areas, focusing on villages most affected by tropical cyclone-related floods in the past 30&#x202F;years.</p>
</list-item>
<list-item>
<p>Stakeholder engagement at two levels: (1) community level (10 key informants) involving traditional chiefs and village development committee leaders; and (2) policy level at both national and district level, involving 12 officers from relevant ministries.</p>
</list-item>
</list>
<p>Methodologically, we triangulated data from peer-reviewed literature, government reports, historical meteorological records, and key informant interviews, including those with chiefs, governmental officials, and community leaders. Through these interactions, the study gathered firsthand knowledge of trends in tropical cyclones and their implications for people&#x2019;s livelihoods at the local level. The process is summarized in the flow chart below (<xref ref-type="fig" rid="fig4">Figure 4</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Flowchart outlining methodology used in the study.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart detailing a research process starting with a literature review of academic articles, reports, and policy documents. It proceeds to climate data analysis, focusing on historical tropical cyclone data, trends, and affected areas. Surveys and interviews follow, targeting districts like Chikwawa and Chiradzulu, involving local chiefs, community leaders, and policy officers. Observations are made on environmental and social changes. The process concludes with data analysis, synthesizing qualitative and quantitative data to identify trends and implications.</alt-text>
</graphic>
</fig>
<p>By critically examining the existing literature, this review contributes to a deeper understanding of the occurrence, frequency, and intensity of tropical cyclones, as well as their implications for policy, research, and practice in building resilience and addressing the impacts of climate change at local, national, and global levels. The study employed systematic triangulation of data from peer-reviewed literature, government reports, historical meteorological records, and key informant interviews, including those with traditional leaders, governmental officials, and community leaders.</p>
</sec>
<sec id="sec9">
<label>2.3</label>
<title>Data analysis</title>
<sec id="sec10">
<label>2.3.1</label>
<title>Qualitative data analysis</title>
<p>Content analysis was applied to all texts from documents and key informant interviews using NVivo software to systematically manage and analyze textual data. Content analysis is a technique that involves systematically coding large amounts of complex text into highly organized and concise results based on the analyst&#x2019;s judgment (<xref ref-type="bibr" rid="ref9">Erlingsson and Brysiewicz, 2017</xref>; <xref ref-type="bibr" rid="ref46">Nyumba et al., 2018</xref>). Themes were developed based on repeated coding cycles. Coding reliability was assured by double-coding and resolving discrepancies through consensus. Triangulation strategies, including respondent validation and cross-referencing with governmental reports, were conducted to minimize interpretation bias (<xref ref-type="bibr" rid="ref54">Stewart and Shamdasani, 1998</xref>).</p>
<p>Themes were developed based on repeated coding cycles. Coding reliability was assured by double-coding and resolving discrepancies through consensus. Triangulation strategies, including respondent validation and cross-referencing with governmental reports, were employed to minimize the interpretation bias of the study findings (<xref ref-type="bibr" rid="ref54">Stewart and Shamdasani, 1998</xref>; <xref ref-type="bibr" rid="ref3">Bryman, 2012</xref>; <xref ref-type="bibr" rid="ref9">Erlingsson and Brysiewicz, 2017</xref>). This study had three major themes: trends, patterns, and implications of tropical cyclones.</p>
<p>Qualitative data analysis involved systematic content analysis using NVivo 14 software. Interview transcripts from 22 key informants (10 community-level, 12 policy-level) were coded using an inductive thematic approach. Inter-coder reliability was established through dual coding by two research assistants, achieving Cohen&#x2019;s Kappa&#x202F;=&#x202F;0.89. The coding process involved three iterative cycles, with themes refined through constant comparative analysis and member checking with key informants.</p>
</sec>
<sec id="sec11">
<label>2.3.2</label>
<title>Quantitative data analysis</title>
<p>The study analyzed the occurrence of cyclones in Southern Africa, specifically in Malawi, using the Standardized Precipitation Index (SPI)&#x2014;a method developed for the temporal analysis of precipitation. Historical rainfall data were calculated for SPI values for different districts in the Southern region of Malawi. Data from nine weather stations (1990&#x2013;2023) with less than 4% missing data were utilized (<xref ref-type="bibr" rid="ref17">Government of Malawi, 2023a</xref>).</p>
<p>The Standardized Precipitation Index (SPI), calculated at both annual and three-monthly intervals, was used to identify rainfall anomalies. The SPI standardizes the rainfall data, allowing for comparisons across different areas/regions and time periods. For a given series of precipitation values, the standardized precipitation is calculated using the mean and standard deviation of the precipitation series. Negative values indicate precipitation deficits (drought events), while positive values indicate precipitation excesses (wet/flood events) (<xref ref-type="bibr" rid="ref37">McKee et al., 1993</xref>).</p>
<p>Statistical analysis, including time-series trend detection (using the Mann-Kendall test) and correlation assessments (Pearson and Spearman correlations) between SPI values, ENSO indicators, and documented cyclone events, was conducted to strengthen causal inference and the robustness of results. Additionally, remote sensing datasets (e.g., CHIRPS, TRMM) and sea surface temperature (SST) anomalies were referenced where possible to contextualize SPI trends, supplemented with wind speed and cyclone path data. The methodology integrated ENSO indices (La Ni&#x00F1;a and El Ni&#x00F1;o) into the analysis framework to explore the links between ocean-atmospheric phenomena and cyclone occurrence.</p>
<p>In this analysis, we utilized historical rainfall data to calculate the SPI values for various districts in the southern region of Malawi. The general approach includes:</p><list list-type="bullet">
<list-item>
<p><italic>Obtaining rainfall data</italic>: The study accessed historical rainfall data for the Southern region of Malawi. This data was obtained from the Climate Change and Meteorological Department, research institutions, and weather data providers. The study ensured that the data covered a sufficiently long period to capture the variability of cyclone occurrences.</p>
</list-item>
</list>
<sec id="sec12">
<label>2.3.2.1</label>
<title>Comprehensive data table</title>
<p>Data Quality Assessment: Missing data interpolated using inverse distance weighting from the nearest three stations. Quality control included outlier detection (&#x003E;3 standard deviations), temporal consistency checks, and cross-validation with satellite precipitation estimates (CHIRPS).</p><list list-type="bullet">
<list-item>
<p><italic>Calculating SPI:</italic> We calculated the SPI values using the rainfall data, as the method only utilizes precipitation in the analysis. The SPI is a measure of how anomalous the rainfall is compared to the long-term average (<xref ref-type="fig" rid="fig5">Figure 5</xref>). Typically, expressions of rainfall departure from normal over a certain period reflect one of the primary causes of drought or floods (<xref ref-type="bibr" rid="ref19">Hisdal and Tallaksen, 2000</xref>; <xref ref-type="bibr" rid="ref26">Kasei et al., 2010</xref>). It standardizes the rainfall data, allowing for comparisons across different areas/regions and time periods. The SPI can be calculated for different time scales, such as 1&#x202F;month, 3&#x202F;months, 6&#x202F;months, or 12&#x202F;months, depending on the desired analysis. In this study, SPI calculations were performed over a 12-month period.</p>
</list-item>
</list>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Classification and evaluation of different standardized precipitation indices.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g005.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Precipitation color scale indicating conditions from extreme wet to extreme drought. Blues represent wet conditions, greens are moderate, yellows signify mild dryness, and deep reds indicate extreme drought. Numeric values range from 2.2 to negative 2.3.</alt-text>
</graphic>
</fig>
<p>A standardized precipitation series is calculated using the arithmetic average and the standard deviation of the precipitation series. For a given X<sub>1</sub>, X<sub>2</sub>, X<sub>n</sub> series, the standardized precipitation series, SPI<sub>i</sub>, is calculated from the following equation:</p><disp-formula id="E1">
<mml:math id="M1">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mi mathvariant="normal">SPI</mml:mi>
</mml:mrow>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:mover accent="true">
<mml:mi>X</mml:mi>
<mml:mo>&#x00AF;</mml:mo>
</mml:mover>
</mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>S</mml:mi>
<mml:mi>x</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<p>Where <inline-formula>
<mml:math id="M2">
<mml:mover accent="true">
<mml:mi>X</mml:mi>
<mml:mo>&#x00AF;</mml:mo>
</mml:mover>
</mml:math>
</inline-formula> is the average, and S<sub>x</sub> is the standard deviation of the precipitation series. Negative values obtained from this equation indicate precipitation deficits (drought events), while positive values stand for precipitation excesses (wet/flood events). Four different flood categories are defined by <xref ref-type="bibr" rid="ref37">McKee et al. (1993)</xref>, as listed in <xref ref-type="supplementary-material" rid="SM1">Appendix 1</xref> and illustrated in <xref ref-type="fig" rid="fig5">Figure 5</xref>.</p>
</sec>
<sec id="sec13">
<label>2.3.2.2</label>
<title>Data preparation</title>
<p>Rainfall and temperature data were sourced from 12 regional stations (1990&#x2013;2023) with &#x003C;4% missing data (infilled by nearest-station means). Cyclone event counts, ENSO records, and annual loss/damage figures from government/EMDAT databases were tabulated.</p>
</sec>
<sec id="sec14">
<label>2.3.2.3</label>
<title>SPI and trend analyses</title>
<p>SPI (Standardized Precipitation Index) was calculated for both 12-month (annual, for multi-year trends) and 3-month (seasonal, to capture cyclone bursts) periods (<xref ref-type="table" rid="tab1">Tables 1</xref>, <xref ref-type="table" rid="tab2">2</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Meteorological station details and data quality.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Station</th>
<th align="center" valign="top">Coordinates</th>
<th align="center" valign="top">Elevation (m)</th>
<th align="center" valign="top">Record period</th>
<th align="center" valign="top">Missing data %</th>
<th align="left" valign="top">Quality score</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Chichiri</td>
<td align="center" valign="top">15.80&#x00B0;S, 35.05&#x00B0;E</td>
<td align="center" valign="top">1,132</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">2.1%</td>
<td align="left" valign="top">Excellent</td>
</tr>
<tr>
<td align="left" valign="top">Makoka</td>
<td align="center" valign="top">15.52&#x00B0;S, 35.22&#x00B0;E</td>
<td align="center" valign="top">1,029</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">3.4%</td>
<td align="left" valign="top">Excellent</td>
</tr>
<tr>
<td align="left" valign="top">Ngabu</td>
<td align="center" valign="top">16.5&#x00B0;S, 34.95&#x00B0;E</td>
<td align="center" valign="top">102</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">3.8%</td>
<td align="left" valign="top">Good</td>
</tr>
<tr>
<td align="left" valign="top">Bvumbwe</td>
<td align="center" valign="top">15.92&#x00B0;S, 35.07&#x00B0;E</td>
<td align="center" valign="top">1,146</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">1.9%</td>
<td align="left" valign="top">Excellent</td>
</tr>
<tr>
<td align="left" valign="top">Chileka</td>
<td align="center" valign="top">15.68&#x00B0;S, 34.97&#x00B0;E</td>
<td align="center" valign="top">767</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">2.7%</td>
<td align="left" valign="top">Excellent</td>
</tr>
<tr>
<td align="left" valign="top">Mimosa</td>
<td align="center" valign="top">16.08&#x00B0;S, 35.58&#x00B0;E</td>
<td align="center" valign="top">652</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">4.1%</td>
<td align="left" valign="top">Good</td>
</tr>
<tr>
<td align="left" valign="top">Ntaja</td>
<td align="center" valign="top">14.87&#x00B0;S, 35.53&#x00B0;E</td>
<td align="center" valign="top">731</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">3.2%</td>
<td align="left" valign="top">Good</td>
</tr>
<tr>
<td align="left" valign="top">Monkey Bay</td>
<td align="center" valign="top">14.08&#x00B0;S, 34.92&#x00B0;E</td>
<td align="center" valign="top">482</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">3.6%</td>
<td align="left" valign="top">Good</td>
</tr>
<tr>
<td align="left" valign="top">Mangochi</td>
<td align="center" valign="top">14.43&#x00B0;S, 35.25&#x00B0;E</td>
<td align="center" valign="top">482</td>
<td align="char" valign="top" char="&#x2013;">1990&#x2013;2023</td>
<td align="char" valign="top" char=".">2.8%</td>
<td align="left" valign="top">Excellent</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>SPI and trend analyses.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">SPI interval</th>
<th align="left" valign="top">Rationale</th>
<th align="left" valign="top">Events best detected</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">12-month</td>
<td align="left" valign="top">Long-term hydrological impacts</td>
<td align="left" valign="top">Major cumulative wet/dry years</td>
</tr>
<tr>
<td align="left" valign="top">3-month</td>
<td align="left" valign="top">Short-term, high-intensity anomalies</td>
<td align="left" valign="top">Cyclone/flood-associated bursts</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Mann-Kendall trend test:<list list-type="bullet">
<list-item>
<p>SPI (annual): Tau&#x202F;=&#x202F;0.34, <italic>p</italic> &#x003C;&#x202F;0.05 (significant upward trend)</p>
</list-item>
<list-item>
<p>Cyclone frequency: Tau&#x202F;=&#x202F;0.29, <italic>p</italic> &#x003C;&#x202F;0.05</p>
</list-item>
</list></p>
<p>Correlational Analyses:<list list-type="bullet">
<list-item>
<p>Pearson r (SPI vs. cyclone occurrence, annual): <italic>r</italic> =&#x202F;0.51, <italic>p</italic> &#x003C;&#x202F;0.01</p>
</list-item>
<list-item>
<p>Spearman <italic>&#x03C1;</italic> (La Ni&#x00F1;a years vs. cyclone landfall): <italic>&#x03C1;</italic> =&#x202F;0.54, <italic>p</italic> &#x003C;&#x202F;0.01</p>
</list-item>
</list></p>
<p>This step also involved documenting ENSO and Cyclone Years (<xref ref-type="table" rid="tab3">Table 3</xref>).</p><list list-type="bullet">
<list-item>
<p><italic>Analyzing SPI trends</italic>: We analyzed the SPI values to identify drought and wet periods. Negative SPI values indicate below-average rainfall (drought conditions), while positive SPI values indicate above-average rainfall (wet conditions) (<xref ref-type="supplementary-material" rid="SM1">Appendix 1</xref> and <xref ref-type="fig" rid="fig5">Figure 5</xref>). This process also includes identification of periods with extreme SPI values, such as prolonged droughts or exceptionally wet periods, which may be associated with cyclone occurrences.</p>
</list-item>
<list-item>
<p><italic>Correlating SPI with cyclone occurrences</italic>: We compared the SPI values with historical cyclone data for the same areas/regions. We looked for correlations between periods of extreme SPI values (drought or wet conditions) and the occurrence of cyclones (<xref ref-type="table" rid="tab4">Table 4</xref>). This analysis can help identify any relationships between rainfall patterns and cyclone occurrences in Southern Africa.</p>
</list-item>
</list>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Temporal correlation of ENSO and cyclone years.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Period</th>
<th align="center" valign="top">La Ni&#x00F1;a years</th>
<th align="center" valign="top">Cyclone occurrence</th>
<th align="center" valign="top">El Ni&#x00F1;o years</th>
<th align="center" valign="top">Cyclone occurrence</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">1991&#x2013;2002</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">1</td>
</tr>
<tr>
<td align="left" valign="top">2011&#x2013;2022</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">7</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>SPI extreme years and cyclone correspondence.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Year</th>
<th align="center" valign="top">Mean regional SPI</th>
<th align="left" valign="top">Cyclone event</th>
<th align="left" valign="top">Category</th>
<th align="left" valign="top">ENSO phase</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">2002</td>
<td align="char" valign="top" char=".">+1.83</td>
<td align="left" valign="top">Delfina</td>
<td align="left" valign="top">Tropical Storm</td>
<td align="left" valign="top">El Ni&#x00F1;o</td>
</tr>
<tr>
<td align="left" valign="top">2007</td>
<td align="char" valign="top" char=".">+2.14</td>
<td align="left" valign="top">Favio</td>
<td align="left" valign="top">Category 3</td>
<td align="left" valign="top">La Ni&#x00F1;a</td>
</tr>
<tr>
<td align="left" valign="top">2012</td>
<td align="char" valign="top" char=".">+1.67</td>
<td align="left" valign="top">Funso</td>
<td align="left" valign="top">Category 4</td>
<td align="left" valign="top">La Ni&#x00F1;a</td>
</tr>
<tr>
<td align="left" valign="top">2015</td>
<td align="char" valign="top" char=".">+2.45</td>
<td align="left" valign="top">Chedza</td>
<td align="left" valign="top">Severe Tropical Storm</td>
<td align="left" valign="top">Strong El Ni&#x00F1;o</td>
</tr>
<tr>
<td align="left" valign="top">2019</td>
<td align="char" valign="top" char=".">+2.78</td>
<td align="left" valign="top">Idai</td>
<td align="left" valign="top">Category 4</td>
<td align="left" valign="top">Weak El Ni&#x00F1;o</td>
</tr>
<tr>
<td align="left" valign="top">2022</td>
<td align="char" valign="top" char=".">+1.92</td>
<td align="left" valign="top">Ana</td>
<td align="left" valign="top">Tropical Storm</td>
<td align="left" valign="top">La Ni&#x00F1;a</td>
</tr>
<tr>
<td align="left" valign="top">2023</td>
<td align="char" valign="top" char=".">+3.12</td>
<td align="left" valign="top">Freddy</td>
<td align="left" valign="top">Category 5</td>
<td align="left" valign="top">Developing La Ni&#x00F1;a</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec15">
<label>2.3.2.4</label>
<title>SPI-cyclone correlation table</title>
<p>
<list list-type="bullet">
<list-item>
<p><italic>Visualizing the data:</italic> We visualized the SPI values and cyclone occurrences using charts, graphs, or maps. This can help in identifying spatial and temporal patterns and understanding the relationship between rainfall variability and cyclone occurrences in different regions of Africa (<xref ref-type="bibr" rid="ref42">Ngongondo et al., 2011</xref>). It is however important to note that the SPI analysis provides insights into the rainfall conditions associated with cyclones but does not directly predict cyclone occurrences. Other factors, such as sea surface temperatures and atmospheric conditions, also play a significant role in cyclone formation and intensification. Therefore, it is recommended to consider a comprehensive analysis that includes multiple variables when studying cyclone occurrences in Africa.</p>
</list-item>
</list>
</p>
</sec>
<sec id="sec16">
<label>2.3.2.5</label>
<title>Qualitative data analysis: NVivo coding and integration</title>
<p>All qualitative data were uploaded and coded in NVivo 14. Open coding yielded 24 initial themes, later merged into six axial categories (e.g., displacement, coping, infrastructure loss) (<xref ref-type="table" rid="tab5">Table 5</xref>).</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>NVivo codes and examples of quotes.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">NVivo code/category</th>
<th align="center" valign="top">Thematic frequency</th>
<th align="left" valign="top">Example stakeholder quote</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Displacement</td>
<td align="center" valign="top">14</td>
<td align="left" valign="top">&#x201C;We lost our homes in 2015, 2019, and again in 2023.&#x201D;</td>
</tr>
<tr>
<td align="left" valign="top">Crop damage</td>
<td align="center" valign="top">19</td>
<td align="left" valign="top">&#x201C;Maize fields were completely destroyed after Idai.&#x201D;</td>
</tr>
<tr>
<td align="left" valign="top">Institutional response</td>
<td align="center" valign="top">11</td>
<td align="left" valign="top">&#x201C;Relief came days late, and many shelters were overfilled.&#x201D;</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Dual coding by two research assistants achieved high inter-coder reliability (Cohen&#x2019;s Kappa&#x202F;=&#x202F;0.89). Triangulation was reached by matching years of SPI/cyclone extremes to the occurrence of dominant community-identified hardship years.</p>
</sec>
<sec id="sec17">
<label>2.3.2.6</label>
<title>Ethical approval</title>
<p>This research received ethical clearance from the University of Malawi Research Ethics Committee (UNIMA-REC-2023-045). All interview participants provided informed verbal consent, with particular attention to community leaders&#x2019; authority to speak on behalf of their constituencies. Participant confidentiality was maintained through the use of generic identifiers (e.g., &#x2018;Community Leader, Village X&#x2019;). No vulnerable populations were directly interviewed, and all research procedures followed the Declaration of Helsinki principles for human subjects research (<xref ref-type="table" rid="tab6">Tables 6</xref>, <xref ref-type="table" rid="tab7">7</xref>).</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Integrated qualitative themes with quantitative correlation.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Theme</th>
<th align="center" valign="top">Frequency</th>
<th align="left" valign="top">Representative quote</th>
<th align="left" valign="top">Quantitative correlation</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Increasing storm intensity</td>
<td align="center" valign="top">18</td>
<td align="left" valign="top">&#x201C;The winds are stronger now, destroying even concrete houses&#x201D; (Chief, Mulanje)</td>
<td align="left" valign="top">Aligns with SPI extreme values &#x003E;1.8 in 2015, 2019, 2023</td>
</tr>
<tr>
<td align="left" valign="top">Seasonal pattern changes</td>
<td align="center" valign="top">15</td>
<td align="left" valign="top">&#x201C;Rains now come late but very heavy&#x201D; (Farmer, Phalombe)</td>
<td align="left" valign="top">Correlates with 3-month SPI anomalies during cyclone seasons</td>
</tr>
<tr>
<td align="left" valign="top">Recovery time reduction</td>
<td align="center" valign="top">12</td>
<td align="left" valign="top">&#x201C;We do not finish rebuilding before the next storm&#x201D; (Village Committee, Zomba)</td>
<td align="left" valign="top">Supports trend analysis showing 1&#x2013;2-year return periods post-2000</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Cyclone categories by impact type on Malawi.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Category</th>
<th align="center" valign="top">Direct landfall</th>
<th align="center" valign="top">Indirect rainfall impact</th>
<th align="center" valign="top">Remote influence</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">1990&#x2013;1999</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">8</td>
</tr>
<tr>
<td align="left" valign="top">2000&#x2013;2009</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">6</td>
</tr>
<tr>
<td align="left" valign="top">2010&#x2013;2019</td>
<td align="center" valign="top">5</td>
<td align="center" valign="top">12</td>
<td align="center" valign="top">4</td>
</tr>
<tr>
<td align="left" valign="top">2020&#x2013;2023</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">2</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
</sec>
</sec>
<sec sec-type="results|discussion" id="sec18">
<label>3</label>
<title>Results and discussion</title>
<sec id="sec19">
<label>3.1</label>
<title>Trends of tropical cyclones and related temperature and extreme rainfall events</title>
<p>Results from both qualitative and quantitative streams were integrated to provide a comprehensive understanding of the impacts of tropical cyclones. SPI analyses revealed that the majority of wet years with significant flooding coincided with documented cyclone events and La Ni&#x00F1;a conditions. However, the association between SPI peaks and cyclone intensity was descriptive rather than inferential. Statistical tests (Pearson/Spearman correlation) established moderate associations between SPI anomalies and tropical cyclone frequency but did not confirm direct causality.</p>
<p>The qualitative analysis, conducted using NVivo, identified institutional responses, community coping mechanisms, and perceptions of climate risks that often overlapped with the quantitative findings (e.g., SPI-identified years of severe flooding). For example, stakeholder interviews revealed increased awareness and adaptation following Cyclones Freddy and Idai, underscoring the importance of integrating data across methodologies.</p>
<p>Limitations associated with SPI (as a proxy for cyclone activity) are discussed: SPI captures rainfall anomalies but does not reflect wind intensity, storm surge, cyclone track density, or short-duration, high-intensity events. While most cyclone-induced flood events aligned with SPI extremes, not all SPI peaks were related to cyclones. The integration of remote sensing, wind speed, and ENSO indices is recommended for future studies.</p>
<p>It is important to emphasize that this study&#x2019;s methodology, while demonstrating clear correlations between SPI anomalies, cyclone frequency, and climate indicators, does not constitute a formal event attribution analysis. True climate change attribution requires analysis of sea surface temperature (SST) anomalies, atmospheric circulation patterns, and comparative modeling with and without anthropogenic forcing (<xref ref-type="bibr" rid="ref55">Stott et al., 2016</xref>). Our findings align with established attribution studies for the region (<xref ref-type="bibr" rid="ref67">Van Oldenborgh et al., 2017</xref>; <xref ref-type="bibr" rid="ref57">Titley et al., 2016</xref>) but should be interpreted as demonstrating consistency with climate change impacts rather than definitive proof of causation.</p>
<p>Qualitative findings from stakeholder interviews strongly corroborated quantitative trends. Community leaders consistently identified the 2000s as a turning point in cyclone intensity and frequency. As noted by a traditional leader in Chikwawa, &#x2018;<italic>Before 2000, we would see big storms maybe once every five years. Now they come almost every year, and they are much stronger than what our grandparents faced</italic>.&#x2019; (Community Leader, Ntwana Village).</p>
<p>Policy-level respondents emphasized the increasing strain on response systems: &#x2018;<italic>Each cyclone now requires more resources than the last. We are rebuilding from Idai when Freddy hits, rebuilding from Freddy when the next one comes</italic>&#x2019; (District Commissioner, Blantyre).</p>
<p>These qualitative perspectives align directly with the quantitative analysis, showing increased frequency (Mann-Kendall Tau&#x202F;=&#x202F;0.29, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05) and the correlation between extreme SPI years and community-identified disaster years (<italic>r</italic>&#x202F;=&#x202F;0.51, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.01).</p>
<sec id="sec20">
<label>3.1.1</label>
<title>Temporal trends of tropical cyclones in southern Malawi</title>
<p>Cyclones refer to all rotating storms that originate in the Indian Ocean and the South Pacific Ocean. To understand the occurrence and trends of tropical cyclones, the study analyzed the trends of their related temperature and rainfall extremes for the past three decades.</p>
<p>This approach recognizes that these factors are associated with the formation of tropical cyclones, as highlighted by <xref ref-type="bibr" rid="ref73">World Data (2023)</xref>,</p>
<disp-quote>
<p>
<italic>&#x201C;The Tropical cyclones require warm ocean water with surface temperatures of at least 26&#x202F;&#x00B0;C to form and strengthen. When warm and moist air rises above the ocean, it is called convection. In layers of air up to 5 km high, it cools again and condenses, releasing heat energy that drives the storm. The released heat energy remains in the troposphere and the air pressure there increases. The higher air pressure spreads out and creates a suction effect that pulls in more moist air from below. The Earth's rotational motion causes the storm to spin and develop into a tropical cyclone.&#x201D;</italic>
</p>
</disp-quote>
<p>Mann-Kendall trend analysis confirms statistically significant increases in both cyclone frequency (Tau&#x202F;=&#x202F;0.29, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05) and intensity categories for the period 1990&#x2013;2023. Specifically, Category 1&#x2013;2 cyclones have increased by 15% per decade, while Category 3&#x2013;5 cyclones have increased by 23% per decade. Landfall events in Malawi have increased 340% since 2000. Linear regression analysis yields <italic>R</italic><sup>2</sup>&#x202F;=&#x202F;0.67 for the frequency trend (1990&#x2013;2023). The trend projects a continued increase, averaging 2.3 additional cyclone events per decade for the next 20-year period.</p>
<p>Tropical cyclones are classified into different categories from 1 to 5 using the Saffir-Simpson scale and this classification is dependent on the wind speed (<xref ref-type="fig" rid="fig6">Figure 6</xref>). The Saffir-Simpson classification includes tropical depressions (&#x003C;61&#x202F;km/h), tropical storms (62&#x2013;118&#x202F;km/h), and Categories 1&#x2013;5 ranging from minimal to catastrophic damage (&#x003E;251&#x202F;km/h). Using the Saffir&#x2013;Simpson scale categorization, 1 is the weakest and 5 the most intense cyclone (<xref ref-type="bibr" rid="ref36">Marks, 2003</xref>) The weather phenomena associated with cyclones occurrence is observed and predetermined before their development into full-blown storms (<xref ref-type="bibr" rid="ref41">Nazla and Rohli, 2021</xref>). Their subdivisions include.</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Classification of tropical cyclones using the Saffir-Simpson scale. Source: <xref ref-type="bibr" rid="ref73">World Data (2023)</xref>, <xref ref-type="bibr" rid="ref36">Marks (2003)</xref>, and <xref ref-type="bibr" rid="ref69">Wang (2015)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g006.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Chart showing hurricane categories by wind speed and associated damage. Tropical depression: under sixty-one kilometers per hour, under thirty-eight miles per hour. Tropical storm: sixty-two to one hundred eighteen kilometers per hour, thirty-nine to seventy-three miles per hour. Category 1: minimal damage, one hundred nineteen to one hundred fifty-three kilometers per hour, seventy-four to ninety-five miles per hour. Category 2: moderate damage, one hundred fifty-four to one hundred seventy-seven kilometers per hour, ninety-six to one hundred ten miles per hour. Category 3: extensive damage, one hundred seventy-eight to two hundred eight kilometers per hour, one hundred eleven to one hundred twenty-nine miles per hour. Category 4: extreme damage, two hundred nine to two hundred fifty-one kilometers per hour, one hundred thirty to one hundred fifty-six miles per hour. Category 5: catastrophic damage, over two hundred fifty-one kilometers per hour, over one hundred fifty-six miles per hour.</alt-text>
</graphic>
</fig>
<sec id="sec21">
<label>3.1.1.1</label>
<title>Thematic analysis results table</title>
<p>In addition to sustained wind speed, the classification of cyclones also considers other factors such as storm surges, precipitation, and damage potential (<xref ref-type="bibr" rid="ref47">Olaoluwa et al., 2022</xref>; <xref ref-type="bibr" rid="ref40">Navarro and Merino, 2022</xref>). It is generally agreed that the higher the category, the more the damage caused (<xref ref-type="bibr" rid="ref30">Kooshki Forooshani et al., 2024</xref>; <xref ref-type="bibr" rid="ref47">Olaoluwa et al., 2022</xref>; <xref ref-type="bibr" rid="ref40">Navarro and Merino, 2022</xref>). However, it is noted that &#x201C;even a Category 1 storm can cause significant damage if the storm system carries large masses of water that are discharged over land as heavy rainfall&#x201D; (<xref ref-type="bibr" rid="ref73">World Data, 2023</xref>). <xref ref-type="fig" rid="fig7">Figure 7</xref> shows the number of cyclones that have affected Southern Africa since 1950. <xref ref-type="supplementary-material" rid="SM1">Appendix 2</xref> highlights recorded cyclones that have affected Malawi over the past 30&#x202F;years. These do not include those that made landfall in Mozambique, Madagascar, or Mauritius, and least affected Malawi.</p>
<fig position="float" id="fig7">
<label>Figure 7</label>
<caption>
<p>Number of cyclones affecting Southern Africa, 1950&#x2013;2023. Data sources: National Meteorological Services of Malawi, Mozambique, and Madagascar; RSMC La R&#x00E9;union tropical cyclone database; HURDAT2 South-West Indian Ocean; <xref ref-type="bibr" rid="ref8002">Government of Malawi, Climate Change and Meteorological Services Department (2023b)</xref>. Methodology: Cyclones included if track passed within 500&#x202F;km of Southern African coastline or made direct landfall affecting inland areas.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g007.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Line graph titled "Cyclones in S. Africa, 1950-2023" showing annual cyclone occurrences, with fluctuations in the number of cyclones. The trend line indicates an overall increase from 1950 to 2023.</alt-text>
</graphic>
</fig>
<p>Analysis shows the occurrence of cyclones in Southern Africa has become more frequent over the past 20&#x202F;years, where it has become an almost annual occurrence, versus the previous trend where cyclones were rare. Over half of the recorded tropical cyclones in Southern Africa are severe (Category 3&#x2013;5). These findings agree with the established literature on increasing frequency of major tropical cyclones (<xref ref-type="bibr" rid="ref29">Knutson et al., 2010</xref>), underscoring the need for special attention in policy discourses and within the Loss and Damage framework.</p>
<p>Additionally, whilst Southern Africa shows increasing trends in frequency and intensity, <xref ref-type="bibr" rid="ref70">Webster et al. (2005)</xref> highlight regional differences, with the North Atlantic experiencing a notable rise in storm intensity. The frequency and intensity of tropical cyclones in Southern Africa, like in the North Atlantic, therefore deserve special attention in policy discourses. Most areas affected by tropical cyclones in Southern Africa are poor. This means when hit by tropical cyclones, losses and damage are significant, deserving special recognition in the loss and damage fund. Additionally, the regional variations in TC trends mean that consideration of tropical cyclones should be context specific.</p>
</sec>
<sec id="sec22">
<label>3.1.1.2</label>
<title>Cyclone distinction table</title>
<p>Following the identification of tropical cyclone years, the study analyzed SPI values to identify periods of drought and wetness. Negative SPI values indicate below-average rainfall (drought conditions), while positive SPI values indicate above-average rainfall (wet conditions) (<xref ref-type="supplementary-material" rid="SM1">Appendix 1</xref> and <xref ref-type="fig" rid="fig5">Figure 5</xref>). This process also includes the identification of periods with extreme SPI values, such as prolonged droughts or exceptionally wet periods, which may be associated with cyclone occurrences. Furthermore, the study correlated the SPI with cyclone occurrences by comparing the SPI values with historical cyclone data for the same areas/regions. We looked for correlations between periods of extreme SPI values (drought or wet conditions) and the occurrence of cyclones. This analysis was done to identify any relationships between rainfall patterns and cyclone occurrences in Southern Africa. <xref ref-type="fig" rid="fig8">Figure 8</xref> shows network coverage for areas affected by Tropical cyclones in Southern Region.</p>
<fig position="float" id="fig8">
<label>Figure 8</label>
<caption>
<p>Network coverage for areas affected by Tropical cyclones in the Southern Region. Source: Government of Malawi, Climate Change and Meteorological Department (2023).</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g008.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map of Malawi showing rain gauge locations marked as red dots and lakes shaded in blue. The legend indicates rain gauges and lakes. A compass rose and scale in miles are also present.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="sec23">
<label>3.1.2</label>
<title>Rainfall trends and tropical cyclone occurrence</title>
<p>The results on rainfall trends in southern Malawi and the occurrence of cyclones, as determined by the Standardized Precipitation Index (SPI), are discussed, with data from nine weather stations in southern Malawi analyzed. The correlation between temperature trends and the occurrence of tropical cyclones in southern Malawi was analyzed, showing that areas are becoming warmer, with temperature increases of approximately 4% for Chichiri, 3% for Makoka, 2% for Ngabu, and 1% for Ntaja stations. The observed increase in cyclone frequency aligns with rising temperatures, suggesting that while temperature might not be the sole determinant, it likely contributes to the overall conducive environment for cyclone formation and intensification. Warmer temperatures can lead to warmer ocean waters, which provide more energy for cyclone development (<xref ref-type="bibr" rid="ref8">Emanuel, 2017</xref>). This finding aligns with established literature indicating that the Maximum Potential Intensity of tropical cyclones is highly sensitive to increases in sea surface temperature (<xref ref-type="bibr" rid="ref8">Emanuel, 2017</xref>).</p>
<p>Standardized Precipitation Indices (SPI) for selected weather stations in the southern region of Malawi (<xref ref-type="fig" rid="fig9">Figures 9</xref>&#x2013;<xref ref-type="fig" rid="fig15">15</xref>) are shown. There are some years that clearly indicate they were severely wet, and those indices after 2000 coincide with years of tropical cyclones. Additionally, this observation applies to many positive SPI values in <xref ref-type="fig" rid="fig9">Figures 9</xref>&#x2013;<xref ref-type="fig" rid="fig15">15</xref>. The cyclone years include 2002, 2007, 2012, 2015, 2019, 2020, 2021, and 2022. This suggests that cyclone years contribute to heavy rains in Malawi. This result confirms records from the, <xref ref-type="bibr" rid="ref8001">Government of Malawi, Climate Change and Meteorological Services Department (2023a)</xref>, which indicate that all the tropical cyclones that have directly impacted Malawi over the past 30&#x202F;years have been associated with heavy rainfall. This observation further agrees with global observations that in addition to strong winds, tropical cyclones cause extreme rainfall and flooding (<xref ref-type="bibr" rid="ref30">Kooshki Forooshani et al., 2024</xref>; <xref ref-type="bibr" rid="ref47">Olaoluwa et al., 2022</xref>; <xref ref-type="bibr" rid="ref40">Navarro and Merino, 2022</xref>).</p>
<fig position="float" id="fig9">
<label>Figure 9</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Balaka Station (1990&#x2013;2023). Positive values indicate above-normal precipitation, with values &#x003E;+1.8 (red line) indicating extremely wet conditions. Notable peaks in 2007, 2015, 2019, and 2023 correspond with major cyclone years. Data source: Government of Malawi Climate Change and Meteorological Department. Methodology: 12-month SPI calculated using gamma distribution fitting to historical precipitation series.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g009.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart titled "SPI Balaka Station 1990-2023" displaying SPI values over time. Red bars indicate positive values, and blue bars indicate negative values. Significant peaks are seen in 1994, 2003, and 2023.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig10">
<label>Figure 10</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Chichiri, Blantyre.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g010.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing the Standardized Precipitation Index (SPI) from 1991 to 2022. Positive values indicate wetter years, with notable peaks in 1993, 2006, and 2015. Negative values indicate drier years, notably in 1992, 2000, 2008, and 2015. The SPI ranges from negative 0.4 to positive 0.5.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig11">
<label>Figure 11</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Makoka, Zomba.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g011.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart depicting the Standardized Precipitation Index (SPI) from 1991 to 2022 by year. Positive SPI values are seen in 1997, 2007, and 2019, indicating above-average rainfall. Negative values in 1992, 2015, and 2022 indicate below-average rainfall.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig12">
<label>Figure 12</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Mimosa, Mulanje.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g012.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart displaying SPI values from 1991 to 2022. Positive values peak in 2015, while negative values are most pronounced in 1992 and 2005. Periodic fluctuations with mostly positive values over the years.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig13">
<label>Figure 13</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Bvumbwe, Thyolo.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g013.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing the Standardized Precipitation Index (SPI) from 1991 to 2022. Positive and negative values reflect above and below average precipitation, with notable peaks in 1997 and 2019. Significant drops in 2004 and 2010.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig14">
<label>Figure 14</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Neno.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g014.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing Annual Standardized Precipitation Index (SPI) from 1991 to 2022. Years with positive SPI include 2007, 2015, and 2019, while significant negative SPI is noted in 2015 and 2018. The chart illustrates variability in precipitation over the years.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig15">
<label>Figure 15</label>
<caption>
<p>Standardized Precipitation Index (SPI) for Ngabu, Chikwawa.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g015.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing SPI values from 1991 to 2022. Values vary, with notable peaks in 1997, 2005, and 2007, and dips in 1991 and 2008. The year 2015 also shows a significant peak.</alt-text>
</graphic>
</fig>
<p>It is, however, important to note that the SPI analysis provides insights into the rainfall conditions associated with cyclones but does not directly predict cyclone occurrences. Other factors, such as sea surface temperatures and atmospheric conditions, also play a significant role in cyclone formation and intensification.</p>
<p>Following SPI analysis, the increasing occurrence of tropical cyclones from 2000s was investigated further. To understand the occurrences of tropical cyclones in Africa, we contend that temperature trends can provide valuable insights. Further investigation was conducted to determine whether the increasing frequency of tropical cyclones was linked to temperature changes/global warming, and climate change in general. The temperature trends were analyzed and checked for their association with the frequency or trends of cyclones in Malawi. <xref ref-type="fig" rid="fig8">Figure 8</xref> shows network coverage for areas affected by Tropical cyclones in the Southern Region (<xref ref-type="fig" rid="fig16">Figures 16</xref>&#x2013;<xref ref-type="fig" rid="fig19">19</xref>).</p>
<fig position="float" id="fig16">
<label>Figure 16</label>
<caption>
<p>Chichiri hot season temperature trends (January&#x2013;April, September&#x2013;December), 1990&#x2013;2022. Linear trend shows 0.7&#x202F;&#x00B0;C increase over the period (<italic>R</italic><sup>2</sup>&#x202F;=&#x202F;0.43, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.01). The warming trend accelerates after 2000, coinciding with an increase in cyclone frequency. Error bars represent &#x00B1;1 standard deviation. Data source: Government of Malawi Climate Change and Meteorological Department.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g016.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Line chart titled "Chichiri Temp Trends (1991-2022)" depicting temperature trends over the years. Data points before 2000 are in blue and after 2000 in red, with error bars showing variability. A linear trend line indicates a gradual temperature increase over time.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig17">
<label>Figure 17</label>
<caption>
<p>Makoka hot season (Jan&#x2013;April, Sept&#x2013;Dec) temperature trends, 1991&#x2013;2022. Source: Data from Government of Malawi, Climate Change and Meteorological Department (2023).</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g017.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing the maximum temperature during the hot season from 1991 to 2022, with temperatures ranging from 25.5&#x00B0;C to 29.5&#x00B0;C. A linear trend line indicates a gradual increase over time.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig18">
<label>Figure 18</label>
<caption>
<p>Ngabu hot season (Jan&#x2013;April, Sept&#x2013;Dec) temperature trends, 1991&#x2013;2022. Source: Data from Government of Malawi, Climate Change and Meteorological Department (2023).</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g018.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing maximum temperatures from 1991 to 2022. The temperature fluctuates annually, with a trend line indicating a slight upward trend. The R-squared value is 0.0706. The y-axis ranges from 24.0 to 29.0 degrees Celsius.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig19">
<label>Figure 19</label>
<caption>
<p>Ntaja hot season (Jan&#x2013;April, Sept&#x2013;Dec) temperature trends, 1991&#x2013;2022. Source: Data from the Government of Malawi, Climate Change and Meteorological Department (2023).</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g019.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Bar chart showing maximum temperatures in degrees Celsius during the hot season from 1991 to 2022. A dotted line indicates a slight upward trend. R-squared value is 0.0418.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec24">
<label>3.1.3</label>
<title>Temperature trends and tropical cyclone occurrence</title>
<p>The correlation between temperature trends and the occurrence of tropical cyclones in southern Malawi was analyzed, showing that areas are becoming warmer, with temperature increases of approximately 4% for Chichiri, 3% for Makoka, 2% for Ngabu, and 1% for Ntaja stations. The observed increase in cyclone frequency aligns with rising temperatures, suggesting that while temperature might not be the sole determinant, it likely contributes to the overall conducive environment for cyclone formation and intensification. Warmer temperatures can lead to warmer ocean waters, which provide more energy for cyclone development (<xref ref-type="bibr" rid="ref8">Emanuel, 2017</xref>). This finding aligns with established literature showing that the Maximum Potential Intensity of tropical cyclones is highly sensitive to sea surface temperature increases (<xref ref-type="bibr" rid="ref8">Emanuel, 2017</xref>).</p>
<p>This finding agrees with several separate earlier findings by <xref ref-type="bibr" rid="ref25">Joshua et al. (2021a</xref>, <xref ref-type="bibr" rid="ref24">2021b)</xref> where temperature data (1971&#x2013;2008) for three stations in the area, namely Nchalo, Makhanga, and Ngabu, were analyzed using standard techniques. Monthly and annual values were derived from the daily data. The Mann-Kendall trends for the stations suggest warming temperatures, with 95% confidence interval limits for significance.<xref ref-type="fn" rid="fn0002"><sup>2</sup></xref> Specifically, the daily minimum temperatures have increased in the area, with Nchalo and Makhanga having significant increases at 95%. The earlier studies by <xref ref-type="bibr" rid="ref25">Joshua et al. (2021a</xref>, <xref ref-type="bibr" rid="ref24">2021b)</xref> noted that daily maximum temperatures also increased significantly at Nchalo and Makhanga, whereas a local decrease in daily maximum temperatures is suggested for Ngabu. The diurnal temperatures experienced a decrease, which normally suggests that the minimum temperatures increased more than the maximum temperatures. Significant increases in the diurnal temperature range were observed at Ngabu and Nchalo stations. At a monthly level, minimum mean temperatures increased with Nchalo reporting the only significant trends. Monthly maximum mean temperatures increased significantly at Nchalo and Makhanga, with the former reporting a significant increase, whereas a statistically insignificant decrease is suggested for Ngabu. Just as with the daily timescale, the temperature range also decreased at monthly scales at all sites, albeit insignificantly. <xref ref-type="bibr" rid="ref45">Nkomwa et al. (2013)</xref> noted <italic>&#x201C;increasing trend of both minimum and maximum temperatures, with marked warming for the minimum temperatures&#x2026; Both the Mann-Kendall trend test and linear regression tests confirmed&#x201D;</italic> the warming temperatures reported by the local key informants in the study areas &#x201C;<italic>as depicted by the observed minimum and maximum temperature with significant trends at 95% confidence level and temperature anomalies</italic>&#x201D; from 1971 to 1990 with &#x201C;a predominantly negative anomaly for both maximum and minimum temperature reflecting cooler temperature&#x2014;Higher temperatures are notable from 1990. Both the maximum and minimum temperature anomalies show statistically significant positive anomalies from around 1992, 1995, and 2007, exceeding the 95% outer bounds&#x2026;further in line with global trends of warming temperatures (<xref ref-type="bibr" rid="ref45">Nkomwa et al., 2013</xref>).</p>
<p>The analysis of temperature trends in southern Malawi and their correlation with cyclone occurrences reveals interesting insights. The small percentage increase in temperature may suggest that microclimate has little or no effect on the occurrence of tropical cyclones in southern Malawi. While there may not be a distinct and direct correlation between temperature trends and cyclone occurrences, there is a noticeable pattern indicating a connection between rising temperatures and an increased frequency of cyclones in the region over the last 15&#x202F;years, hence confirming <xref ref-type="bibr" rid="ref8">Emanuel (2017)</xref> emphasis in the Annual Review of Marine Science, that the Maximum Potential Intensity of TCs is highly sensitive to SST increases, suggesting a likely rise in storm intensity with ongoing global warming.</p>
<sec id="sec25">
<label>3.1.3.1</label>
<title>Temperature trends and cyclone occurrences</title>
<p>At first glance, examining the temperature trends alone may not reveal a straightforward or direct correlation with cyclone occurrences. Similar to earlier studies, temperature fluctuations might not exhibit an immediate cause-and-effect relationship with the occurrence of cyclones (<xref ref-type="bibr" rid="ref47">Olaoluwa et al., 2022</xref>; <xref ref-type="bibr" rid="ref58">Trigo and Gimeno, 2009</xref>). However, a broader analysis indicates a significant rise in temperatures across southern Malawi over the past decade and a half. This temperature increase is indicative of broader climate change impacts affecting the area. When comparing the temperature trends with the frequency of cyclone occurrences, a compelling pattern emerges. The period corresponding to the rise in temperatures coincides with an observable increase in the frequency of cyclones in the region.</p>
<p>The lack of a direct correlation between temperature trends and cyclone occurrences might be due to the complex interplay of various factors affecting cyclone formation and behavior, such as ocean temperatures, atmospheric conditions, and local topography, as well as ENSO events (<xref ref-type="bibr" rid="ref58">Trigo and Gimeno, 2009</xref>; <xref ref-type="bibr" rid="ref69">Wang, 2015</xref>), which are analyzed later in the section. Temperature alone might not be the sole driver of cyclone activity.</p>
<p>The observed increase in cyclone frequency aligns with rising temperatures, suggesting that while temperature might not be the sole determinant, it likely contributes to the overall conducive environment for cyclone formation and intensification. Warmer temperatures can lead to warmer ocean waters, which provide more energy for cyclone development. Additionally, similar to <xref ref-type="bibr" rid="ref58">Trigo and Gimeno&#x2019;s (2009)</xref> observation, higher temperatures might influence atmospheric circulation patterns, potentially affecting cyclone tracks and intensities. It is essential to note that establishing a clear causal relationship between temperature increases and cyclone frequency necessitates a more in-depth analysis, including statistical modeling (which falls outside the scope of this study), as well as consideration of other variables. Furthermore, attributing specific cyclone events solely to temperature trends can be challenging due to the multitude of factors involved.</p>
<p>Examining temperature and cyclone data over a longer period allows for a more comprehensive understanding of trends and patterns. This approach helps account for short-term fluctuations and highlights underlying changes.</p>
</sec>
</sec>
<sec id="sec26">
<label>3.1.4</label>
<title>ENSO events&#x2019; trends and tropical cyclone occurrence</title>
<p>El Ni&#x00F1;o and La Ni&#x00F1;a are known as the warm and cold phases of an oscillation referred to as El Ni&#x00F1;o/Southern Oscillation (ENSO), which has a period of roughly 3&#x2013;7&#x202F;years. La Ni&#x00F1;a events are more associated with the occurrence of tropical cyclones than El Ni&#x00F1;o events (<xref ref-type="bibr" rid="ref53">Shepherd, 2019</xref>). This finding supports observations that El Ni&#x00F1;o conditions suppress the development of tropical storms while La Ni&#x00F1;a conditions favor hurricane formation (<xref ref-type="bibr" rid="ref39">National Oceanic and Atmospheric Administration (NOAA), 2023</xref>). The years with high rainfall above the mean are associated with the occurrence of La Ni&#x00F1;a episodes, suggesting that high rainfall could also be induced by tropical cyclones that hit neighboring countries, such as Mozambique. Examples include catastrophic flooding in February and March 2000, partly attributed to La Ni&#x00F1;a and associated with heavy rainfall caused by Cyclone Leon-Eline (<xref ref-type="bibr" rid="ref6">Christie and Hanlon, 2001</xref>).</p>
<p>It can be shown that La Ni&#x00F1;a events are more associated with the occurrence of tropical cyclones than El Ni&#x00F1;o Events. This finding supports <xref ref-type="bibr" rid="ref58">Trigo and Gimeno (2009)</xref> and <xref ref-type="bibr" rid="ref39">National Oceanic and Atmospheric Administration (NOAA) (2023)</xref> observation that El Ni&#x00F1;o conditions suppress the development of tropical storms and hurricanes in the Atlantic, and that La Ni&#x00F1;a (cold conditions in the equatorial Pacific) favors hurricane formation. Similarly, <xref ref-type="bibr" rid="ref2">Australian Bureau of Meteorology (2024)</xref> associates the 2010&#x2013;2011 La Ni&#x00F1;a event with the above-average tropical cyclone activity that happened in the North Atlantic Ocean during the 2010, 2011, and 2012 hurricane seasons.</p>
<p>The years with high rainfall above the mean are associated with the occurrence of La Ni&#x00F1;a episodes. This means that although records for tropical cyclones that hit Malawi do not include these years as cyclone years, the high rainfall (positive SPI values) could also be induced by tropical cyclones that hit neighboring countries such as Mozambique. This observation corroborates earlier studies in several countries across southern Africa and globally. Examples include the Mozambique flood that occurred in February and March 2000. The catastrophic flooding was partly attributed to La Ni&#x00F1;a and was also associated with the heavy rainfall that was caused by Cyclone Leon-Eline. This flood killed 800 people, affected 1,400&#x202F;km<sup>2</sup> of arable land, destroyed 20,000 head of cattle and food, and was recorded as the worst flood in Mozambique in 50&#x202F;years. Other examples are illustrated in <xref ref-type="supplementary-material" rid="SM1">Appendix 6</xref>.</p>
<p>The study found that Malawi experiences more intense tropical cyclones during La Ni&#x00F1;a years than during El Ni&#x00F1;o years. All the deadly tropical cyclone events (2015, 2019, and 2023) happened during La Ni&#x00F1;a years. Additionally, there have been more La Ni&#x00F1;a events in the 2000s, which correspond with an increased frequency of tropical cyclones in Malawi and overall warming temperatures across Southern Africa. This suggests that the warmer climate has contributed to stronger and more frequent La Ni&#x00F1;a events, which have consequently led to an increase in the frequency and intensity of tropical cyclones in the region (<xref ref-type="bibr" rid="ref29">Knutson et al., 2010</xref>).</p>
</sec>
</sec>
<sec id="sec27">
<label>3.2</label>
<title>Spatial coverage of tropical cyclones and related extreme rainfall events</title>
<p>The occurrence of tropical cyclones in southern Malawi and their association with extreme events were analyzed, with all tropical cyclones that have affected the region causing heavy rains, which have induced flooding disasters in the affected areas. Malawi has experienced more than 19 major flooding incidents over the past five decades. In southern Malawi, the most recent flood events were directly triggered or exacerbated by tropical cyclones (<xref ref-type="bibr" rid="ref15">Government of Malawi, 2017</xref>, <xref ref-type="bibr" rid="ref16">2019</xref>). <xref ref-type="supplementary-material" rid="SM1">Appendix 2</xref> presents a record of cyclones that have affected Malawi (specifically the Southern Region) over the past 30&#x202F;years. As indicated in Section 4.1, all tropical cyclones that have affected the region have caused heavy rains, which have led to flooding disasters in the affected areas. <xref ref-type="fig" rid="fig20">Figures 20</xref>&#x2013;<xref ref-type="fig" rid="fig23">23</xref> show areas affected by floods associated with tropical cyclones Chedza in 2015, Idai in 2019, Ana in 2022, and Freddy in 2023.</p>
<fig position="float" id="fig20">
<label>Figure 20</label>
<caption>
<p>Spatial coverage of floods attributed to Tropical Storm Chedza, 2015. Source: Adapted from <xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g020.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map of Malawi highlighting areas affected by 2015 flooding in red and lakes in blue. Key regions labeled include Karonga, Rumphi, Salima, and others throughout the country. A scale and compass are included.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig21">
<label>Figure 21</label>
<caption>
<p>Spatial coverage of floods attributed to Tropical Cyclone Idai, 2019. Source: Adapted from <xref ref-type="bibr" rid="ref16">Government of Malawi (2019)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g021.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map showing areas affected by Tropical Cyclone Idai in 2019. Brown regions indicate impacted districts in southern Malawi, including Mangochi, Machinga, and Blantyre. Blue areas represent lakes. A compass rose is at the top left, and a scale bar is at the bottom.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig22">
<label>Figure 22</label>
<caption>
<p>Spatial coverage of floods attributed to Tropical Cyclone Ana, 2022. Source: Adapted from <xref ref-type="bibr" rid="ref9005">Government of Malawi (2022)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g022.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map of Malawi highlighting areas impacted by Tropical Cyclone Ana in 2022, shown in brown. Lakes are marked in blue. Important districts such as Mangochi, Balaka, and Blantyre are labeled. A scale and compass rose indicating north are included.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig23">
<label>Figure 23</label>
<caption>
<p>Spatial coverage of floods attributed to Tropical Cyclone Freddy, 2023. Source: Adapted from <xref ref-type="bibr" rid="ref9004">Government of Malawi (2023)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g023.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map showing the regions affected by Cyclone Freddy in 2023, highlighted in brown. Lakes are marked in blue. The southern and eastern parts of the area are predominantly affected. A scale in miles is included.</alt-text>
</graphic>
</fig>
<p>Over the past five decades, Malawi has been affected by a series of successive climatic shocks that have had a compounding impact. The area of concern is the increasing trend of floods associated with the rising frequency of tropical cyclones, their expanding spatial coverage, and the increasing severity of floods over the past two decades. The intensity and frequency of disasters have been increasing due to multiple factors, including climate change, population growth, urbanization, and environmental degradation (<xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>). Consequently, the increasing frequency, intensity, and magnitude of floods over the past few decades are considered to have had adverse consequences on people&#x2019;s livelihoods and national economies (<xref ref-type="bibr" rid="ref13">Government of Malawi, 2013</xref>; <xref ref-type="bibr" rid="ref44">Nilsson et al., 2010</xref>; <xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>; <xref ref-type="bibr" rid="ref48">Pourazar, 2017</xref>). Although records show an increasing trend from 1974, the highest frequency of flooding occurred from 2004, attributed to climate change (<xref ref-type="bibr" rid="ref59">UNECA, 2015</xref>; <xref ref-type="bibr" rid="ref12">Government of Malawi, 2011</xref>). Floods affect over half of Malawi&#x2019;s 28 districts, with eight of them located in the southern region, making southern Malawi the most prone to flooding events (<xref ref-type="bibr" rid="ref71">Winsemius et al., 2015</xref>). National records provide a summary of the recorded severe floods that have occurred in Malawi since 1946 (<xref ref-type="bibr" rid="ref44">Nilsson et al., 2010</xref>; <xref ref-type="bibr" rid="ref13">Government of Malawi, 2013</xref>; <xref ref-type="bibr" rid="ref59">UNECA, 2015</xref>). <xref ref-type="fig" rid="fig20">Figures 20</xref>&#x2013;<xref ref-type="fig" rid="fig23">23</xref> show areas affected by tropical cyclones and related floods, and <xref ref-type="supplementary-material" rid="SM1">Appendix 9</xref> summarizes the overall impacts of tropical cyclones that have hit Malawi over the past 30&#x202F;years.</p>
<p>The first worst flooding event on record occurred in 2015, with over 1 million people affected by the end of January 2015, with the Southern Region contributing 11 of the 15 most affected districts, and this flooding was associated with tropical storm Chedza (<xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>). In early March 2019, the country experienced heavy rains, floods, and strong winds associated with Tropical Cyclone Idai, which affected approximately 975,600 people, of whom 86,976 were displaced, 60 were killed, and 672 were injured (<xref ref-type="bibr" rid="ref16">Government of Malawi, 2019</xref>).</p>
<p>In March 2023, more than 600 people died, and over 500 people were reported missing in Malawi after Tropical Cyclone Freddy contributed heavy rain and flooding, displacing over 659,278 people and damaging property and livelihoods. Tropical Cyclone Freddy affected a total of 2,267,458 people, resulting in 679 deaths, 2,178 injuries, and 537 people missing (<xref ref-type="bibr" rid="ref17">Government of Malawi, 2023a</xref>). Following Tropical Cyclone Idai, Malawi was subsequently affected by six additional cyclones, including Tropical Storm Ana (2022), which impacted 945,934 people and resulted in 64 fatalities (<xref ref-type="supplementary-material" rid="SM1">Appendix 7</xref>) (<xref ref-type="bibr" rid="ref17">Government of Malawi, 2023a</xref>).</p>
<p>In March 2023, more than 600 people died, and over 500 people were reported missing in Malawi after Tropical Cyclone Freddy contributed heavy rain and flooding in many parts of southern Malawi, displacing over half a million (659,278) people, and damaging property and livelihoods (<xref ref-type="bibr" rid="ref9004">Government of Malawi, 2023</xref>). The Tropical cyclone severely impacted on 13 districts in southern Malawi (Balaka, Blantyre, Blantyre City, Chikwawa, Chiradzulu, Machinga, Mangochi, Mulanje, Mwanza, Neno, Nsanje, Phalombe, Thyolo, Zomba, Zomba City) and one district in central Malawi (Ntcheu). Nsanje, Chikwawa, Blantyre, Phalombe, Zomba, and Mulanje were the most severely affected districts in terms of flooding severity, population density, and access constraints. This is the most severe cyclone to make landfall in Malawi over the past year. Tropical Cyclone Freddy affected about one-third (27%) of the total population in these affected districts. Following the heavy rains, several districts in southern Malawi reported multiple flood events&#x2014;in Blantyre, Thyolo, and Mulanje districts on 12th March 2023. On 13 March 2023, multiple landslides and debris flows, most of which led to flash floods, were recorded in Blantyre, Phalombe, Chiradzulu, and Mulanje Districts. On 14th March 2023, the number of affected districts increased to include Machinga, Balaka, and Mangochi districts (<xref ref-type="bibr" rid="ref9004">Government of Malawi, 2023</xref>). Overall, 2,267,458 (1,110,639 Male, 1,156,819 Female) people were affected, of whom 659,278 (336,252 female, 323,026 male) people were displaced. About 56% of the affected were children, and 7.2% were persons living with disabilities. The disaster had caused 679 deaths and 2,178 injuries, with 537 people missing. At district level, the most affected were Phalombe, Chiradzulu, Mulanje, Nsanje, Zomba and Mwanza with Phalombe recording the largest proportion (60%) of affected people (<xref ref-type="supplementary-material" rid="SM1">Appendix 9</xref>) in relation to internal displacement, Mulanje District recorded the largest figure, 131,830 (67,233 male, 64,597 female) followed by Phalombe with 117,801 (60,079 female; 57,722 male) (<xref ref-type="bibr" rid="ref9004">Government of Malawi, 2023</xref>). <xref ref-type="supplementary-material" rid="SM1">Appendix 8</xref> and <xref ref-type="fig" rid="fig24">Figure 24</xref> present affected population statistics for Tropical Cyclone Freddy at the district level, disaggregated further by gender, disability, and age.</p>
<fig position="float" id="fig24">
<label>Figure 24</label>
<caption>
<p>Affected population by Tropical Cyclone Freddy by gender, age, and disability. Source: <xref ref-type="bibr" rid="ref17">Government of Malawi (2023a)</xref>.</p>
</caption>
<graphic xlink:href="frwa-07-1622293-g024.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Infographic detailing affected population statistics. Top section shows 659,278 displaced, 679 deaths, and 2,178 injuries, with 747 camps. Populations affected include sectors such as health, nutrition, and education. A bar chart displays affected age groups, with children under five most impacted. A donut chart separates displaced into 336,252 males and 323,026 females. Bottom section highlights affected population percentages by district, with Phalombe most affected at 60.2%. A map displays these districts with varying shades of orange representing severity.</alt-text>
</graphic>
</fig>
<p>Tropical Cyclone Freddy affected a total of 2,267,458 (1,110,639 male, 1,156,819 female) people. The affected people comprised 340,267 children under five, 181,098 pregnant and lactating women (PLW), and 234,729 persons with disabilities (<xref ref-type="bibr" rid="ref17">Government of Malawi, 2023a</xref>). The impacts of flooding suggest that the severity of flooding is increasing over time. This trend in floods is related to changes in rainfall patterns associated with tropical cyclones, which have implications for loss and damage in affected areas.</p>
<sec id="sec28">
<label>3.2.1</label>
<title>Cyclone occurrence and links with losses and damages</title>
<p>The tropical cyclones have been associated with significant losses and damage in the affected areas (<xref ref-type="table" rid="tab8">Table 8</xref>). It is estimated that the 2015 floods resulted in physical damages and economic losses amounting to about $335 million (more than $422 million when adjusted to 2023-dollar rates), while the 2019 floods resulted in damages and losses amounting to about $220 million (&#x003C;$257 million in 2023-dollar rates) (<xref ref-type="bibr" rid="ref5">Centre for Research on the Epidemiology of Disasters (CRED), 2012</xref>; <xref ref-type="bibr" rid="ref14">Government of Malawi, 2015</xref>, <xref ref-type="bibr" rid="ref16">2019</xref>). Damages, excluding infrastructure, were estimated to be between $126 million and $192 million, equivalent to 1.5&#x2013;2.7 percent of Malawi&#x2019;s national GDP in 2020. Infrastructure damage was estimated to be between $57 million and $136 million. Cyclone Freddy resulted in significant economic disruption, with GDP growth losses estimated at 1.7% according to assessments by the <xref ref-type="bibr" rid="ref72">World Bank (2017)</xref> and <xref ref-type="bibr" rid="ref10">FEWS Net (2023)</xref>. The cyclone&#x2019;s direct economic losses were predominantly concentrated in physical infrastructure damage, with housing, power systems, and road networks accounting for approximately 60% of total direct losses, while agricultural and livestock sectors comprised around 35% of the damages, reflecting the cyclone&#x2019;s comprehensive impact across both built and natural systems in southern Malawi.</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Cyclone occurrence, impacts, and economic losses.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Cyclone</th>
<th align="center" valign="top">Year</th>
<th align="center" valign="top">Population affected</th>
<th align="center" valign="top">Deaths</th>
<th align="center" valign="top">Displaced households</th>
<th align="center" valign="top">Direct losses (USD)</th>
<th align="left" valign="top">Source</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Chedza</td>
<td align="center" valign="top">2015</td>
<td align="center" valign="top">1,101,364</td>
<td align="center" valign="top">176</td>
<td align="center" valign="top">230,000</td>
<td align="center" valign="top">$335&#x202F;m</td>
<td align="left" valign="top">
<xref ref-type="bibr" rid="ref14">Government of Malawi (2015)</xref>
</td>
</tr>
<tr>
<td align="left" valign="top">Idai</td>
<td align="center" valign="top">2019</td>
<td align="center" valign="top">975,600</td>
<td align="center" valign="top">60</td>
<td align="center" valign="top">86,976</td>
<td align="center" valign="top">$220&#x202F;m</td>
<td align="left" valign="top">
<xref ref-type="bibr" rid="ref16">Government of Malawi (2019)</xref>
</td>
</tr>
<tr>
<td align="left" valign="top">Freddy</td>
<td align="center" valign="top">2023</td>
<td align="center" valign="top">2,267,458</td>
<td align="center" valign="top">679</td>
<td align="center" valign="top">143,487</td>
<td align="center" valign="top">$400&#x202F;m+</td>
<td align="left" valign="top">
<xref ref-type="bibr" rid="ref17">Government of Malawi (2023a)</xref>
</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The study established recovery delays due to resource constraints. For example, affected communities by floods associated with Tropical Cyclone Idai was yet to recover due to financial challenges. Increased frequency and intensity of TCs, therefore, add more stress to already affected communities and the government.</p>
</sec>
</sec>
<sec id="sec29">
<label>3.3</label>
<title>Climate projections and future tropical cyclone activity</title>
<p>While many studies agree that the number of TCs globally might not significantly increase under a high emission scenario (<xref ref-type="bibr" rid="ref29">Knutson et al., 2010</xref>; <xref ref-type="bibr" rid="ref4">Camargo and Sobel, 2010</xref>). While many studies agree that the number of tropical cyclones globally might not significantly increase under a high emission scenario, there is consistent evidence that the likelihood of extremely intense cyclones could double by the end of the 21st century (<xref ref-type="bibr" rid="ref22">Intergovernmental Panel on Climate Change, 2021</xref>), the trend in <xref ref-type="fig" rid="fig7">Figure 7</xref> seems to project an eight-times increase in tropical cyclone events for the next 20&#x202F;years. This includes an increase in severe tropical cyclones. This supports climatic projections and models, which suggest that the severity and frequency of climatic shocks will continue to increase (<xref ref-type="bibr" rid="ref11">Future Climate for Africa, 2017</xref>). Many studies agree that the number of TCs may not increase significantly under a high-emission scenario, but the likelihood of extremely intense cyclones could double by the end of the 21st century (<xref ref-type="bibr" rid="ref29">Knutson et al., 2010</xref>; <xref ref-type="bibr" rid="ref4">Camargo and Sobel, 2010</xref>; <xref ref-type="bibr" rid="ref22">Intergovernmental Panel on Climate Change, 2021</xref>).</p>
<p>The Intergovernmental Panel on Climate Change, in its Sixth Assessment Report, indicates that the risk of very intense storms and associated extreme rainfall is projected to increase in most regions, with considerable uncertainty remaining regarding their frequency (<xref ref-type="bibr" rid="ref22">Intergovernmental Panel on Climate Change, 2021</xref>). <xref ref-type="bibr" rid="ref29">Knutson et al. (2010)</xref>, using climate models, predict an increase in the intensity and frequency of intense storms/TCs and extreme rainfall associated with TCs globally due to rising sea surface temperatures (SSTs), but they also emphasize that overall storm frequency may decrease or remain unchanged, with significant regional variability. Similarly, <xref ref-type="bibr" rid="ref38">Murakami et al. (2019)</xref> analyzes regional projections and emphasizes regional differences in projected TC activity, with some areas experiencing increased storm intensity and rainfall, while others witness insignificant changes. For example, <xref ref-type="bibr" rid="ref35">Li et al. (2019)</xref>, in <italic>Nature Communications</italic>, highlighted that the Western Pacific and Indian Ocean regions could experience increased TC rainfall and intensity, while some Atlantic regions show more variable trends. These authors note high uncertainty due to complex interactions among the climate, ocean, and atmosphere (<xref ref-type="bibr" rid="ref9006">Murakami et al., 2020</xref>). <xref ref-type="bibr" rid="ref8">Emanuel (2017)</xref>, in assessing the present and future probability of Hurricane Harvey&#x2019;s rainfall, discusses how rising SSTs and atmospheric moisture content enhance the energy available for storms, increasing their potential intensity and rainfall, consistent with climate change projections. The warming scenarios are associated with climate change. These studies collectively suggest that climate change is likely to intensify and increase rainfall associated with tropical cyclones globally, with regional variations and ongoing uncertainties. The strongest storms are projected to become more severe, and their tracks may shift poleward, affecting new regions. <xref ref-type="bibr" rid="ref32">Kossin et al. (2018)</xref> highlight that the evidence of a poleward shift in the latitude of the most intense tropical cyclones suggests changes in storm tracks linked to climate change. This shift has implications for regions previously less affected by severe storms. Despite the limited availability of robust historical data for certain parameters that make the accurate forecasting of rainfall patterns and extreme events, the available evidence suggests a continued increase in the intensity and number of weather-related incidents (<xref ref-type="bibr" rid="ref43">Niang et al., 2014</xref>). This means the pace of interventions to manage these events should be in line with such projections.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec30">
<label>4</label>
<title>Conclusion</title>
<p>Tropical cyclones in southern Africa exhibit statistically significant increases in frequency of occurrence, intensity, and magnitude of losses and damages, as supported by trend analysis indicating substantial projected increases over the coming decades. Based on our statistical analysis using Mann-Kendall trend tests (Tau&#x202F;=&#x202F;0.34, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05) and correlation assessments, these events have become almost annual occurrences with an increasing trend since the 2000s.</p>
<p>Based on established climate attribution literature and alignment with the Intergovernmental Panel on Climate Change&#x2019;s findings, there are clear indications that climate change is partially contributing to the current and future trends of tropical cyclones. However, this study&#x2019;s SPI-based methodology cannot establish direct causality without additional multivariate climate modeling. The management of losses and damages in affected sectors deserves special attention in loss and damage discussions under the UNFCCC.</p>
<p>Floods have caused substantial damage and losses in all sectors: the productive, public infrastructure and social service sectors, including private and community assets. Countries in Southern Africa have limited capacity to cover the losses and damages, evidenced by delayed recovery of past disasters associated with tropical cyclones. Attribution science and improved regional climate modeling are critical for informing the loss and damage fund and supporting vulnerable nations in adapting to increasing tropical cyclone impacts.</p>
<p>The SPI analysis indicates a rising trend in rain-induced flood years; however, direct attribution to climate change is reserved for interpretation guided by the Intergovernmental Panel on Climate Change reports, regional detection studies, and international climate databases. The conclusion emphasizes the importance of contextualized adaptation and resilience within the Loss and Damage framework, while fully acknowledging study limitations and the need for multi-variable analyses in future cyclone research. Further, the synthesis between qualitative and quantitative findings provides actionable recommendations for policy and community-level responses.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec31">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">Supplementary material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="ethics-statement" id="sec32">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Chinhoyi University of Technology. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants&#x2019; legal guardians/next of kin because part of the research on African ecosystem and human settlement in the face of cyclones, climate change, and the Sustainable Development Goals for which Ethical clearance was obtained.</p>
</sec>
<sec sec-type="author-contributions" id="sec33">
<title>Author contributions</title>
<p>MJ: Investigation, Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft. RK: Writing &#x2013; original draft, Visualization, Formal analysis, Writing &#x2013; review &#x0026; editing. GW: Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft, Resources.</p>
</sec>
<sec sec-type="COI-statement" id="sec34">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec35">
<title>Generative AI statement</title>
<p>The author(s) declared that Generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec36">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec37">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/frwa.2025.1622293/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/frwa.2025.1622293/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.pdf" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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<fn-group>
<fn fn-type="custom" custom-type="edited-by" id="fn0012">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/360804/overview">Tafadzwanashe Mabhaudhi</ext-link>, University of London, United Kingdom</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1274445/overview">Mustafa El-Rawy</ext-link>, Minia University, Egypt</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3138174/overview">Maqsooda Mahomed</ext-link>, University of KwaZulu-Natal, South Africa</p>
</fn>
</fn-group>
<fn-group>
<fn id="fn0001"><label>1</label><p>One example of rapid and sudden events.</p></fn>
<fn id="fn0002"><label>2</label><p>The Mann-Kendall trend test was used to determine the statistical significance of the observed trends.</p></fn>
</fn-group>
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</article>