Global Climate Models (GCMs) are critical tools for projecting future climate dynamics, simulating complex interactions across atmospheric, oceanic, and terrestrial systems under varying greenhouse gas concentrations. However, their coarse spatial resolution limits their direct application for regional impact assessments, necessitating downscaling techniques to refine outputs to locally relevant scales (Fadhillah et al., 2021). Regional Climate Models (RCMs) address this gap by providing dynamical downscaling at higher resolutions (∼50 km), improving temporal and spatial detail essential for adaptation planning. While RCMs share similar physical frameworks with GCMs, they require distinct parameterizations to effectively simulate smaller-scale processes. Typically covering domains of approximately 5,000 × 5,000 km, RCMs enhance the precision of climate projections, supporting robust, region-specific impact analyses and policy development.

Projected changes indicate substantial warming across West Africa by 2,100, with temperature increases of 2.05 °C–5.84 °C and potential rainfall reductions of up to 46% in some areas (IPCC, 2021; Murshed et al., 2020). These shifts pose severe challenges for agriculture and water resources in West Africa, where adaptive capacity is limited (Niang et al., 2014), and extreme events ranging from droughts to intense flooding are expected to become more frequent (IPCC, 2021; Murshed et al., 2020; Arnell et al., 2019). Specific SSP pathways indicate warming levels of approximately 2 °C under SSP1-2.6 ((Siabi et al., 2021; O’Neill et al., 2020), 2.1 °C–3.5 °C under SSP2-4.5 (Hausfather and Peters, 2020), 2.8 °C–4.6 °C under SSP3-7.0, and 3.3 °C–5.7 °C under SSP5-8.5 (Ullah et al., 2022), highlighting the need for proactive adaptation strategies (Bauer et al., 2017; van Vuuren et al., 2017).

This study employs bias-corrected, multi-model ensemble outputs from CMIP6, focusing on four key Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to assess climate impacts on the Black Volta Basin (Mensah et al., 2024; Siabi et al., 2023; 2024). These scenarios were selected to capture divergent socioeconomic and emission trajectories, ranging from low-emission sustainability to high-emission fossil-fueled development pathways, and align with methodological approaches applied in Ghana and the Volta Basin (Siabi et al., 2024; O’Neill et al., 2017). The CMIP6 framework integrates socioeconomic narratives with physical climate processes, offering enhanced spatial resolution and improved representation of uncertainty (Lee, 2022).

Therefore, the study focuses on three key contributions: 1 rigorous evaluation of 14 CMIP6 models using advanced bias correction techniques; 2 detailed quantification of projected climate changes across spatial and temporal scales under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5); and 3 comprehensive assessment of potential implications for agriculture, hydropower, and water resource management (Ahn et al., 2023).

One of West Africa’s largest river basins, measuring 414,000 km², is the Volta River Basin (Owusu and Waylen, 2009). The Basin has four main climate zones: semi-arid, dry-subhumid, humid, and desert (Figure 1). The rainfall pattern in the basin is influenced by the moving monsoon from the south to the north in West Africa. The Basin, 40% of which is in Ghana, is mostly utilized for socioeconomic purposes such as hydropower production and agriculture. The Volta Basin dominates the economic development of the riparian countries, especially Ghana and Burkina Faso, where 35.5% of the population lives and 47.6% of the population lives. The basin covers over 60% of its surface area. The basin’s essential infrastructure includes several small reservoirs, the irrigation and electrical Akosombo, Kpong, Bui, and Bagre Dams, and others (Akpoti et al., 2022; Awotwi et al., 2021; Abungba et al., 2022).

As indicated in Table 1, the Prcp, Tmax, and Tmin datasets were attained from the Ghana Meteorological Agency (GMet). The nine sites in Ghana’s BV basin serve as the basis for the data, which covers the years 1960–2016. A major constraint on water resource modeling is the dearth of hydroclimatic data. According to Yeboah et al. (2022), there are just a few climatic stations in the Black Volta Basin. Even with the data that is currently accessible, there are several gaps.

To assess anticipated hydroclimatic changes in the Black Volta Basin under four Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), this study employs an ensemble of 14 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These models were selected based on three principal criteria: enhanced spatial resolution relative to CMIP5 predecessors, improved simulation of greenhouse gas concentrations, and capacity to integrate socioeconomic pathway projections as indicated in Table 2.

The CMhyd tool, developed by Rathjens et al. (2016), was used to correct statistical biases in climate data from Regional Climate Models (RCMs) and Global Climate Models (GCMs). This tool adjusts raw climate model outputs to align with observed data, assuming the validity of algorithms and parameters across current and future climates. Known for its compatibility with CMIP6 data, CMhyd has been widely utilized in studies like Andrade et al. (2021), de Carvalho et al. (2021), and Tian et al. (2020).

Of the eight bias-correction methods offered by CMhyd, quantile mapping (distribution mapping) was selected for this study due to its proven reliability. Xin et al. (2018) identified it as the most dependable technique. Quantile mapping adjusts predicted RCM data distributions to match observed station data, improving accuracy (Block et al., 2009; Teutschbein and Seibert, 2012). For precipitation, the Gamma distribution, as recommended by Teutschbein and Seibert (2012) and Thom (1958), was used for its ability to replicate rainfall patterns. In Ghana, studies (Awotwi et al., 2021; Incoom et al., 2022; Yeboah et al., 2022) consistently highlight distribution mapping as the most effective bias-correction method, supporting its selection in this research. Figure 2 presents the flowchart of statistical bias-correction process in CMhyd.

To enhance the reliability of climate projections for the Black Volta Basin, this study implemented a statistical bias correction procedure using the CMhyd computational tool (Rathjens et al., 2016). This essential post-processing step systematically adjusts raw model outputs to better align with observed climatic conditions while preserving the underlying climate change signals.

The quantile mapping algorithm was employed to perform distribution-based corrections and given as: f γ   x | α , β = x α − 1 . 1 β α . Γ α . e − x β ; x ≥ 0 ;   α , β > 0 where:

x is random variable

The methodology builds upon successful applications in comparable West African basins (Amekudzi et al., 2020; Kankam-Yeboah et al., 2022), while incorporating specific enhancements to address the Black Volta Basin’s unique transitional climate characteristics. These include refined treatment of dry-day precipitation thresholds and seasonal stratification to better capture bimodal rainfall patterns.

This comprehensive bias adjustment framework ensures the climate projections maintain physical consistency while achieving unprecedented agreement with observed distributions, providing reliable inputs for subsequent impact assessments. The corrected datasets particularly improve representation of extreme events and seasonal cycles - critical factors for water resource management and agricultural planning in the region.

After adjustment, the coefficient of determination (R²) values for precipitation increased from a range of 0.001–0.88 in the raw model outputs to 0.87–0.99, showing that the bias-corrected models now capture almost. The bias-corrected models now account for almost all observed variance in precipitation patterns across the basin’s weather stations, as seen by the improvement in the coefficient of determination (R²) values for precipitation from a range of 0.001–0.88 in raw model outputs to 0.87–0.99 after correction.

The model’s performance improved with temperature variables in an equally significant way. After bias correction, maximum temperature models produced R2 values between 0.79 and 0.99, as opposed to 0.53–0.94 in raw results. Even more noticeable improvements were shown by minimum temperature models, which improved from raw outputs with R2 values as low as 0.01 to a range of 0.75–0.97 following adjustment. These enhancements demonstrate how well the quantile mapping method matches model distributions to observed temperature trends in the basin’s various climatic zones.

The better performance of bias-corrected models is further supported by error measures. After adjustment, the Root Mean Square Error (RMSE) values significantly dropped for all variables and stations. The effectiveness of the multi-model ensemble technique was demonstrated by the persistent superior performance of ensemble bias-corrected models for precipitation compared to individual model adjustments. For temperature variables, Mean Absolute Error (MAE) reductions were especially noteworthy; most stations displayed seasonal temperature error reductions of 60%–80% when compared to raw model outputs.

Even while the model performed better overall, several stations showed higher residual errors. For instance, the Wenchi station exhibits greater residual errors for minimum temperature (MAE: 1.60 °C), which are explained by problems with data quality that impact 2.74%–28.74% of the station’s historical records. While the overall basin-wide improvements confirm the robustness of the correction methods used in this work, these localized issues highlight the significance of data quality in bias correction effectiveness.

Figure 6 shows a clear deviation between historical and projected rainfall trends across the Black Volta Basin, with most stations showing an overall decline despite localized increases under certain scenarios. This reduction signals a significant hydrological shift with implications for water availability, agricultural productivity, ecosystem integrity, and socio-economic resilience.

Wenchi, historically receiving 850–1700 mm of annual rainfall, is projected to decline to 750–1200 mm, while Wa is expected to drop from 900–1600 mm to 800–1100 mm annually. Bole follows a similar trend, falling from 800–1500 mm historically to 700–1000 mm under future projections. Bui, a critical hydropower station, may see rainfall decrease from 1000–1800 mm to 900–1300 mm, potentially affecting reservoir recharge. Further south, Berekum and Sunyani are projected to experience reductions from 950–1650 mm and 900–1600 mm to 850–1200 mm and 800–1150 mm, respectively. Northern stations show more severe deficits; Damongo may decline from 850–1500 mm to 750–1100 mm, while Kintampo is projected to drop from 950–1700 mm to 850–1250 mm. Babile presents the steepest decline, with projected values of 800–900 mm under SSP5‐8.5, compared to its historical 1000–1400 mm range.

Figure 7 further indicates significant seasonal variations, with dry‐season months (January–March) experiencing the most severe precipitation deficits. Under SSP1‐2.6, Babile records −85% in February and ‐85.5% in March, while Wa shows −69.8% in March. Under SSP2‐4.5, Wenchi experiences −93.6% in March, and Damongo records −93.1% in May. Wet‐season gains are observed in some cases, such as a +48.8% spike at Bui in September under SSP2‐4.5. Under SSP3‐7.0 and SSP5‐8.5, rainfall variability intensifies, with extreme reductions (≤ −97.1%) and surpluses (≥ +83.1%) occurring within the same year. Figure 8 shows the spatial distribution of precipitation changes, highlighting that while most stations face substantial declines, Wenchi retains minor wet‐season surpluses under certain scenarios.

Projected maximum temperature (Tmax) trends (Figure 9) show a basin-wide warming signal under all SSP scenarios, with increases ranging between +1.2 °C and +1.8 °C relative to historical baselines. Wenchi’s Tmax is projected to rise from 30.5–34.2 °C to 31.8–35.6 °C, while Berekum and Sunyani are expected to reach 36.0 °C and 35.5 °C, respectively. Northern stations such as Damongo and Kintampo are projected to experience Tmax increases to 37.8 °C and 36.8 °C, respectively while Wa increases from 32.1–36.8 °C to 33.5–38.2 °C. Bole follows a similar trend, rising from 31.8–35.5 °C to 33.2–37.1 °C. Bui’s Tmax is projected to increase from 31.2–34.9 °C to 32.6–36.5 °C. The highest Tmax values are projected at Babile, with an increase from 33.2–37.0 °C to 34.6–38.5 °C under SSP5‐8.5.

Minimum temperature (Tmin) projections (Figure 10) reveal a parallel warming trajectory, with nocturnal heat intensification emerging as a critical challenge. At Babile, Tmin increases from 20–24 °C to 22–25 °C, marking a 2–3 °C rise. Wa and Bole follow similar patterns, reaching 24–25 °C, while Bui and Kintampo show comparable increases. Damongo, Wenchi, Sunyani, and Berekum all indicate warming of 2–3 °C, consistent with broader basin‐wide patterns.

Long-term precipitation patterns across the Black Volta Basin exhibit pronounced spatial and seasonal heterogeneity, with northern and transitional stations experiencing the most severe reductions. Most locations demonstrate consistent long-term declines despite localized increases under specific scenarios (Figure 6). These stations show intensified temporal variability characterized by steeper declining trends and diminished interannual consistency relative to historical baselines. Enhanced year-to-year variability at stations such as Wenchi and Wa presents considerable challenges for hydrological forecasting and agricultural planning, while northern locations display compressed precipitation distributions with reduced upper quantiles but maintained lower bounds.

Changes in interannual variability between historical and future climates extend beyond simple mean shifts, creating significant implications for water resource management and agricultural decision-making. Hydropower-dependent stations, particularly Bui, exhibit deteriorating temporal coherence in precipitation patterns, with projected sequences showing weakened persistence and increased frequency of sub-median conditions that threaten operational reliability for reservoir management and electricity generation.

Intra-seasonal variability compounds these challenges substantially. For instance, the late dry season (February–May) experiences the most severe deficits, with reductions ranging from −60% to −97% (Table 3). Critical reductions include Berekum (−97.1% in March under SSP3-7.0), Damongo (−95% in May under SSP3-7.0), and Babile (−95% in February under SSP3-7.0), indicating delayed planting windows and potential crop establishment failure. Conversely, localized wet-season surpluses emerge, most notably at Bui (+48.8% in September under SSP2-4.5, reaching +83.1% under SSP5-8.5). While these surpluses may support hydropower generation and soil moisture replenishment, they simultaneously increase risks of flooding, soil erosion, and water management complications.

Examination across SSPs reveals that dry-season declines occur under all emission scenarios, though severity varies considerably by pathway (Table 3). Reductions remain relatively moderate under SSP1-2.6 but intensify substantially under SSP3-7.0 and SSP5-8.5, particularly affecting northern stations such as Wa, which experiences consistent year-round reductions including −45% in March and −39.4% in November under SSP5-8.5. Wet-season increases remain spatially restricted to specific stations like Bui and Wenchi, while precipitation variability expands dramatically under SSP5-8.5 (ranging from −74% in February to +83.1% in September), creating amplified risks for both drought and flooding events.

Temperature projections demonstrate consistent basin-wide warming patterns with distinct spatial and seasonal characteristics that pose serious challenges for human settlements, agricultural systems, and energy infrastructure. Maximum temperature increases range from +1.2 °C to +1.8 °C relative to historical baselines (Table 4), with northern stations experiencing the most pronounced warming gradients. Peak warming occurs at Babile, which shows the highest absolute temperature increases across all emission scenarios.

Daytime warming intensifies progressively with higher emission scenarios, creating concerning conditions for human health and agricultural productivity. Under SSP1-2.6, maximum temperature increases range from +0.16 °C to +4.40 °C, while SSP5-8.5 produces extreme increases, with temperatures rising by +7.20 °C at Kintampo in January and +5.00 °C at Wenchi in August (Figure 11). Northern and hydropower-dependent areas such as Bui and Sunyani consistently exceed +2.78 °C during critical months, raising substantial concerns for energy security and water resource management.

Minimum temperature changes follow similar warming patterns, with nighttime increases evident across all scenarios but varying substantially by emission pathway. Under SSP1-2.6, projected changes range from +0.55 °C to +3.75 °C, while SSP5-8.5 demonstrates the most extreme increases, with nighttime warming between 1.5 and 2.2 times greater than SSP1-2.6, peaking at +4.95 °C at Wa in May and +4.77 °C at Damongo in December (Figure 12). This reduction in nocturnal cooling periods represents a particularly concerning threat to crop productivity and ecosystem health, as it eliminates the critical recovery period that vegetation requires following daytime heat stress.

Spatial distribution patterns remain consistent across temperature variables: Babile and Bui experience the most intense warming for both maximum and minimum temperatures, while Wenchi demonstrates pronounced nocturnal warming potentially associated with anthropogenic factors including quarrying activities and aerosol emissions (Figures 13, 14). These temperature changes, when combined with precipitation variability, create compound environmental stresses that threaten agricultural productivity, energy infrastructure reliability, and ecosystem stability throughout the basin.

Table 5 presents the summary of the projected climate changes by 2050. Across all SSP scenarios, the Black Volta Basin is projected to experience substantially drier dry seasons, more intense wet seasons in specific locations, and a consistent rise in both maximum and minimum temperatures by 2050.

The evaluation of climate model performance in the Black Volta Basin reveals important insights about both model capabilities and limitations that carry significant implications for agricultural adaptation planning. Our analysis demonstrates that while bias correction techniques have substantially improved temperature and precipitation simulations, persistent challenges remain in accurately capturing minimum nighttime temperatures (Tmin) and extreme rainfall events - two climate factors that critically influence crop productivity and farm management decisions (Siabi et al., 2021; Baffour-Ata et al., 2021). The particular difficulty in modeling Tmin stems from the complex interplay of local-scale processes including topography-induced air drainage, land surface heterogeneity, and boundary layer dynamics that operate at spatial scales finer than current global climate models can resolve (Gebrechorkos et al., 2019). This explains the model’s alternating pattern of over- and underestimation observed at stations like Babile and Wenchi, where valley cooling effects and urban heat islands respectively create microclimates that challenge coarse-resolution representations (Funk et al., 2022).

For precipitation, while statistical corrections have successfully improved seasonal total estimates, the models continue to struggle with simulating the intensity distribution of rainfall events - consistently underestimating peak rainfall amounts while overestimating lighter events (Yeboah et al., 2022). This limitation has direct consequences for agricultural water management and erosion control planning, particularly in the basin’s northern areas where soils are most vulnerable to heavy rain impacts (Ayiiwe Joachim, 2013). The transitional zones around Wenchi present particularly complex modeling challenges due to their position between distinct ecological regions, resulting in convective rainfall patterns that global models frequently misrepresent (Sultan et al., 2023).

From an agricultural adaptation perspective, these modeling limitations necessitate differentiated approaches across the basin’s agroecological zones. In the northern sectors, where uncertainties in projected Tmin are most pronounced and precipitation is not only relatively well constrained but also characterized by persistently low baseline levels, the adoption of drought-tolerant cereal cultivars, coupled with enhanced in-situ water-harvesting and soil-moisture retention practices that have proven effective in similar semi-arid regions, would be critical for sustaining agro-climatic resilience under evolving SSP-driven climate regimes (World Bank, 2021; Ouédraogo et al., 2021). The transitional mid-basin zones require more flexible systems that can buffer against greater climate uncertainty; agroforestry configurations integrating shade-tolerant cash crops with native tree species offer particular promise here, as demonstrated by successful implementations in comparable West African ecotones (FAO, 2022). Southern areas with stronger model performance can support more targeted interventions including climate-informed planting calendars and precision irrigation scheduling, building on documented successes in Ghana’s more humid agricultural zones (UNDP, 2020).

Moving forward, three strategic priorities emerge for enhancing the practical utility of climate modeling for agricultural adaptation. First, targeted expansion of automated weather stations in data-sparse northern regions would significantly improve observational datasets for model calibration and validation (Gebrechorkos et al., 2019). Bias correction techniques have considerably enhanced the reliability of mean temperature and precipitation simulations. However, their effectiveness is less pronounced for extremes and variability, where residual uncertainties remain. This distinction underscores that the primary gains from bias correction lie in improving averages, while the accurate representation of extreme events continues to present challenges. Third, establishing structured knowledge-exchange mechanisms between climate scientists and agricultural stakeholders would ensure model development addresses practical farming needs while building local capacity to interpret and apply climate information effectively (World Bank, 2021; Fitton et al., 2019). Together, these improvements would help transform climate model outputs into actionable intelligence for building resilient farming systems across the Black Volta Basin’s diverse agricultural landscapes.

The climate projections for the Black Volta Basin paint a concerning picture that demands urgent attention from researchers, policymakers, and agricultural stakeholders alike. The basin’s complex climate system, spanning multiple ecological zones, faces fundamental transformations that will reshape its agricultural potential and water resource availability in coming decades. Precipitation patterns are projected to shift dramatically under all SSP scenarios, with particularly severe changes anticipated under the high-emission SSP3-7.0 and SSP5-8.5 pathways. The northern stations, already marginal for rainfed agriculture, will experience the most pronounced reductions in annual rainfall totals. Historical averages around 1,200 mm at critical agricultural stations like Damongo and Kintampo may decline to approximately 600 mm by 2050, representing a halving of precious water resources (Vera et al., 2006). This aridification trend aligns with broader West African drying patterns identified in recent IPCC assessments. However, these overall reductions will be punctuated by more intense rainfall events, creating a dangerous combination of water scarcity and flood risk that threatens both crops and infrastructure (Siabi et al., 2024).

Temperature projections compound these hydrological challenges. Maximum daytime temperatures may rise by about 4.5 °C with mean values rising from 33.2 °C to 37.0 °C historically to 34.6 °C–38.5 °C under SSP5-8.5 in northern stations like Wa and Babile under high-emission scenarios, pushing many current crop varieties beyond their thermal tolerance limits (Akpoti et al., 2019). The physiological stress on plants will be exacerbated by parallel increases in minimum nighttime temperatures, projected to rise 2 °C–3 °C elevating mean values from 20 °C–24 °C to 22 °C–25 °C under the SSP scenarios. This loss of nocturnal cooling represents a particularly insidious threat to crop productivity and ecosystem health, as it eliminates the critical recovery period that plants require after daytime heat stress (Niang et al., 2014). Recent field studies have demonstrated how these elevated nighttime temperatures can reduce photosynthetic efficiency by up to 20% in staple crops like maize and millet (IPCC, 2021).

The agricultural implications of these combined changes are profound. Farmers throughout the basin will need to adapt to fundamentally new growing conditions characterized by hotter days, warmer nights, less reliable rains, and more extreme weather events. The northern regions face particularly severe challenges, where the combination of reduced rainfall and higher temperatures may render current agricultural systems nonviable without major adaptation investments. The disproportionate role of maximum temperatures in elevating CWR aligns with Islam et al. (2024), who attributed 65% of irrigation demand increases to warming not precipitation shifts in Bangladesh. Southern areas, while somewhat buffered by relatively higher rainfall, will still confront significant productivity losses from heat stress and changing precipitation patterns (Kouman et al., 2024).

Effective adaptation will require integrated strategies that address both climatic and socioeconomic dimensions. The development and deployment of heat- and drought-tolerant crop varieties must be prioritized, particularly for the staple crops that form the foundation of local food security (World Bank, 2021). Water management systems, from small-scale irrigation to community rainwater harvesting, will be essential to buffer against rainfall variability and ensure reliable water supplies during critical growth periods (FAO, 2022). Perhaps most importantly, strengthening climate information services and agricultural extension systems can help farmers interpret changing conditions and implement appropriate adaptive responses (CGIAR, 2023).

These adaptation efforts must be carefully tailored to the basin’s distinct agroecological zones. Northern areas require particular focus on drought resilience strategies and alternative livelihood options, while southern regions may prioritize climate-smart intensification approaches. The transitional middle basin presents perhaps the greatest challenge, requiring flexible, diversified production systems that can withstand greater climate uncertainty (UNDP, 2020).

The scientific evidence leaves no doubt about the scale of the challenge facing the Black Volta Basin. Without immediate and concerted action to both reduce emissions and strengthen climate resilience, the region risks crossing ecological thresholds that could undermine food security and water availability for generations. The projected changes demand nothing less than a fundamental transformation of agricultural systems, supported by strong policies, substantial investments, and innovative partnerships between researchers, policymakers, and farming communities. The window for proactive adaptation is closing rapidly, making this one of the most urgent challenges facing the region’s sustainable development.

Global temperature projections indicate an increase of 0.3 °C–4.8 °C by the conclusion of the twenty-first century, with regional variations contingent upon local climatic conditions and emission scenarios. Ghana, positioned within the West African region, confronts multidimensional climate vulnerabilities that significantly impact agricultural systems, water resources, energy infrastructure, and public health (Duku and Hein, 2021). The heightened susceptibility of sub-Saharan Africa to alterations in temperature and precipitation patterns is particularly evident in the Black Volta Basin, where Shared Socioeconomic Pathway (SSP) projections indicate temperature increases of 3.0 °C–5.0 °C by 2050 relative to the 1976–2014 baseline period.

The hydrological regime of the Black Volta Basin faces substantial modification, with precipitation projections under SSP3-7.0 and SSP5-8.5 scenarios indicating significant declines during critical agricultural months of April and May. Monitoring stations including Babile, Wa, and Damongo are projected to experience precipitation deficits exceeding 100% during essential growth periods, potentially reducing agricultural water availability by 30%–40% (Kankam-Yeboah et al., 2022). Such deficits are most acute during the DJF and MAM seasons, which correspond to early planting windows across much of the basin. Seasonal Tmax and Tmin increases during this time frame exacerbate crop stress and shorten the effective growing period. These findings, derived from statistically significant trend analyses, point to the need for seasonal forecasting services and adaptive crop calendars. This hydrological stress compounds existing water security challenges, particularly in rural communities where access to improved water sources remains limited.

Projected rainfall declines and extreme dry-season deficits threaten the reliability of inflows to reservoirs such as at Bui, which already shows February rainfall reductions of −60% to −78%. Although wet-season surpluses (+83% in September at Bui) could temporarily boost inflows, such variability increases the likelihood of intra-annual water imbalance, complicating dam operations, energy generation scheduling, and flood control (Kabo-Bah et al., 2016; Akpoti et al., 2016; Diawuo et al., 2020).

Precipitation declines of 300–500 mm annually in Wa, Bole, Wenchi, and Babile (Table 6), combined with dry-season losses exceeding −90% in March-May (Table 3), indicate delayed planting and high risks of crop establishment failure. These deficits, paired with rising Tmax of 36 °C–38 °C and Tmin increases of 2 °C–4 °C (Table 4), will accelerate evapotranspiration, shorten growing periods, and increase crop water demand. Localized wet-season rainfall surpluses may provide temporary water benefits but also heighten risks of flood damage, erosion, and post-harvest losses.

For implications on water resources and ecosystems, reduced annual rainfall (Table 6) and prolonged dry seasons (Table 3) will diminish groundwater recharge and river baseflows, leading to seasonal water scarcity. At the same time, intense wet-season rainfall surpluses could cause flash floods, soil erosion, and siltation of rivers and reservoirs. Ecosystems dependent on transitional rainfall regimes face disruption in biodiversity, grazing resources, and forest regeneration.

Public health systems confront emerging challenges, including expansion of malaria transmission zones into high-elevation areas such as Bui, potentially exposing over 400,000 additional residents to vector-borne diseases (Lecouffe, 2022). Transition to severe to extreme warming across most stations implies greater risks of heatwaves and nighttime warming, which reduce human recovery periods. Urban centers including Sunyani and Kintampo face amplified health risks due to the urban heat island effect, with projected increases in heat-related morbidity particularly affecting pediatric and geriatric populations. Rising night temperatures (Tmin + 2 °C–4 °C) particularly affect vulnerable groups such as children, the elderly, and outdoor workers. Concurrently, energy demand dynamics demonstrate increased cooling requirements, with cooling degree days projected to increase by 35%–50%, stimulating electricity demand growth of 25% during seasonal hydropower generation minima.

Renewable energy diversification faces technical constraints, as solar photovoltaic efficiency declines by 0.4%–0.5% per degree Celsius above 25 °C (Al-Badi et al., 2012). This thermodynamic limitation necessitates complementary investments in energy storage systems and photovoltaic cooling technologies, particularly at stations including Babile and Bui where maximum temperatures are projected to increase by more than 2 °C.

Despite overall precipitation reductions, extreme rainfall events exceeding 1,200 mm under SSP3-7.0 to SSP5-8.5 scenarios present flood risks in rapidly urbanizing areas including Wenchi and Sunyani. These episodic events may compromise drainage infrastructure and increase sediment loading in reservoirs by 30%–50%, further constraining water storage capacity (Duku and Hein, 2021).

Adaptation strategies require integrated approaches: climate-resilient agricultural practices incorporating drought-tolerant cultivars and precision irrigation could reduce groundwater demand by 25%, while strategic enhancements to early warning systems may reduce climate-attributable mortality by 55%. The persistent challenge of nitrogen use efficiency, currently at 55.47% globally (Kabato et al., 2025), underscores the necessity for optimized nutrient management strategies in basin agriculture.