This study investigates the precipitation characteristics of tropical cyclones (TCs) making landfall in East China from 1960 to 2010.
Methods
Statistical and factor analysis methodologies were employed, leveraging TC precipitation separation datasets and global reanalysis data.
Results
The findings indicate that while the annual total precipitation from landfalling TCs is relatively stable, the maximum daily precipitation at individual stations shows a gradual increasing trend. The frequency of TC impacts and the occurrence of TCs inducing extreme precipitation (≥250 mm) follow a distinct "single peak" monthly distribution. Spatially, stations with the highest frequencies of heavy rain are concentrated along the southeastern coast, with intensity weakening inland. Northward-turning TCs after landfall exert the most significant influence on local wind and rainfall. Compared to weak-precipitation TCs, strong-precipitation TCs are characterized by slower movement, higher wind speeds, and lower central pressure.
Discussion
The results further demonstrate that TCs developing under favorable thermal, dynamic, and moisture conditions tend to generate more intense precipitation.
landfalling TCEast ChinaTC precipitationspatiotemporal distributionenvironmental factorsThe authors declare that financial support was received for the research and/or publication of this article. This study was supported by the Natural Science Foundation of Fujian (Grants 2023J01186, 2022J01445), the Natural Science Foundation of Xiamen (Grants 3502Z202474025, 3502Z202572057).section-in-acceptanceAtmospheric ScienceIntroduction
China is among the countries most severely affected by tropical cyclones (TCs), with an average of seven to eight TCs making landfall each year (Chen and Ding, 1979). These systems often trigger heavy rainfall, leading to flash floods, urban inundation, infrastructure damage, and substantial economic losses. Over recent decades, China has witnessed significant impacts from TC-induced rainfall, as illustrated by several severe events. For example, TC Fred (9417) caused extensive damage in Wenzhou (Zhang and Xu, 1996), TC Longwang (0519) brought extreme rainfall to Fuzhou (Lin et al., 2007), and TC Haitang (0505) resulted in direct economic losses exceeding 7.8 billion RMB in Wenzhou (Yu et al., 2007).
To mitigate TC disasters and improve rainfall prediction, considerable research has been conducted on the climatic characteristics of landfalling TCs. Li and Chen (2004) analyzed the frequency, location, and life cycle of TCs impacting China from the western Pacific. Cheng et al. (Cheng et al., 2007; Cheng et al., 2012) examined the spatiotemporal variations in TC precipitation and its interaction with the summer monsoon. Dong (2010) noted that rainfall amplification near landfalling TCs is markedly asymmetric, with key amplification centers within five degrees of the TC center, especially in the northeast and southwest quadrants. Yuan et al. (2011) also studied TCs making landfall in or near China.
While these studies have yielded valuable insights, they often focus on large-scale analyses, leaving a gap in regional-specific climate characteristics—particularly for East China, which is highly vulnerable to TC rainfall disasters. Therefore, enhancing research on the distribution patterns and physical mechanisms of TC rainfall in this region is essential to improve predictive accuracy and disaster resilience.
This paper uses TC precipitation separation data and the National Centers for Environmental Prediction (NCEP) global reanalysis data to analyze the precipitation characteristics of TCs that made landfall in six provinces and one municipality in East China. By focusing on TCs that specifically affect East China, the study systematically derives the temporal and spatial distribution characteristics of TC precipitation and conducts a comparative analysis of related factors influencing the occurrence and development of precipitation. This research aims to provide scientific references for understanding the formation mechanisms of TC precipitation in East China.
Data and methodologyData
The TC daily precipitation separation data from 534 stations in six provinces and one municipality in East China from 1960 to 2010 used in this study is based on the OSAT (Objective Synoptic Analysis Technique) method for identifying TC precipitation (Ren et al., 2001; Ren et al., 2007). This method for identifying TC precipitation is mainly carried out in two steps: 1. identifying the precipitation field structure of the TC process and dividing the precipitation field into different rainbands; 2. determining the TC precipitation based on the distance relationships between these rainbands and the TC center, as well as between each station and the TC center. The daily boundary for precipitation data is defined as the period from 20:00 Beijing Time of the preceding day to 20:00 Beijing Time of the current day. The TC track data is obtained from the best track dataset provided by the Shanghai TC Institute.
Methodology
To study the precipitation impact of landfalling TCs more scientifically, this paper uses volumetric precipitation (VP) for analysis and discussion. The VP calculation method used in this study follows the inverse distance interpolation method as employed by Ren et al. (2002). This method mainly involves the following steps: first, interpolate the TC precipitation data into grid point data with a 0.5° × 0.5° grid; second, calculate the area corresponding to each grid point and the VP; finally, sum the VP of all grid points within the affected area to obtain the total VP. The specific calculation formula is as follows:A=r2·cosφ·Δφ·Δλ=πr/3602·cosφVP=A×P×10−6where A and VP denote the grid area and volumetric precipitation for each grid point, respectively, A is measured in km2 and VP in km3. In the formulas, r,ϕ,λ, and p are the Earth’s radius (km), the latitude, longitude, and precipitation amount (mm) corresponding to each grid point, respectively.
Additionally, this study uses the dynamic composite method referenced from Gray (1979) and Li et al. (2005) to perform composite analysis on the environmental fields of different TCs. The NCEP reanalysis data is used for this purpose, with a temporal resolution of four times per day, a horizontal resolution of 2.5° × 2.5°, and 17 vertical layers. By following the TC center, a series of dynamic environmental fields centered on the TC center are obtained. The composite mean environmental field for each group of TCs is obtained by averaging the sum of all sample fields (Figure 1). The specific calculation formula is as follows:S¯tx,y=1N∑n=1NStx,y
Schematic diagram of TC tracks (black arrows) and dynamic regions (black rectangles, with black dots representing TC centers).
Diagram showing two series of overlapping squares with curves passing through them. Arrows point from the bottom to the top of each series. There are plus signs between the series and equations with summation symbols on the right.
In these formulas, S¯tx,y is the sample mean of the environmental fields at multiple time points, N is the total number of samples, Stx,y is the environmental field of each sample at time t, and x,y is the selected region range.
Temporal distribution characteristics of TC precipitationInterannual variation
Figure 2 depicts the total VP (a) and the maximum daily station precipitation (b) corresponding to the 137 TCs that made landfall in East China during the period 1960–2010. Each point represents the precipitation brought by a TC that made landfall in East China. From the linear fitting trend, it can be seen that the total VP from TCs in East China remains relatively stable, while the maximum daily station precipitation is gradually increasing. Additionally, the frequency of TCs bringing a total VP greater than 30 km3 and a maximum daily station precipitation greater than 250 mm is increasing year by year. Our results indicate a marginally significant trend (p = 0.1) toward more severe precipitation impacts from TCs making landfall in East China, warranting further investigation with larger datasets or longer time series.
Total VP (a) and maximum daily station precipitation (b) of TCs that made landfall in East China from 1960 to 2010.
Two scatter plots compare precipitation trends from 1960 to 2010. Plot (a) displays total volumetric precipitation in cubic kilometers, showing a slight increase. Plot (b) shows maximum daily station precipitation in millimeters, also indicating a slight upward trend. Both plots include a linear regression line and correlation coefficients.Monthly variation
By analyzing the monthly distribution of TCs that made landfall in East China (Figure 3), it is evident that both the frequency of TCs and the frequency of TCs bringing extreme precipitation (≧250 mm) exhibit a “single peak” monthly distribution. Starting from May, East China begins to be affected by landfalling TCs. The period from July to September is the peak season for TCs making landfall in East China, with the maximum occurring in August, followed by a gradual decrease. The seasonal distribution of TCs bringing extreme precipitation (≧250 mm) aligns with the overall TC frequency, with July to September being the peak period for extreme precipitation from TCs, reaching its peak in August.
Monthly distribution of TCs that made landfall in East China from 1960 to 2010.
Line graph showing the frequency of tropical cyclones (TCs) and those bringing extreme precipitation (≥250 mm) over months. The blue line indicates a peak in TC frequency at 60 in August, while the red dashed line shows TCs with extreme precipitation peaking at about 18 in August. Frequencies drop to nearly zero in other months.Spatial distribution characteristics of TC precipitation
Analyzing the spatial distribution of TC precipitation in East China (Figure 4) reveals that the maximum frequency of both heavy rain and torrential rain—defined herein as precipitation events with daily totals of 50–99.9 mm and ≥100 mm, respectively, following the meteorological standards of the China Meteorological Administration—recorded at weather stations is primarily concentrated along the southeastern coastal areas. A clear decreasing trend in their frequency is observed from the coast to the inland. The junction of Zhejiang and Fujian provinces shows the highest frequency of extreme precipitation, with heavy rain frequency exceeding 75 times and torrential rain frequency exceeding 32.5 times. This indicates that the southeastern coast is most severely affected by landfalling TCs. Additionally, the eastern coast of the Shandong Peninsula, central and western Anhui, and northern Jiangxi also exhibit some smaller high frequency zones of heavy rain (small red circles in Figure 4a).
Spatial distribution of TC precipitation in East China ((a) frequency of heavy rain (≧50 mm) at weather stations, (b) frequency of torrential rain (≧100 mm) at weather stations, (c) maximum daily precipitation at weather stations) and topographical elevation of eastern China ((d) shaded, units: m).
Four maps labeled (a) to (d) highlight precipitation and geographical data in a region of China. Map (a) shows gradual precipitation with a color scale from 0 to 75 millimeters. Map (b) shows a different precipitation scale from 0 to 32.5 millimeters. Map (c) uses shaded circles to represent precipitation levels ranging from under 100 millimeters to over 400 millimeters. Map (d) is a topographic map indicating elevation with a color gradient; several areas are circled in red across all maps.
The distribution of maximum daily station precipitation (Figure 4c) also shows that stronger maximum daily station precipitation is mainly concentrated in the southeastern coastal areas and the Yangtze River Delta, weakening from the coast towards the inland. The southeastern part of Fujian and the coastal junction of Zhejiang and Fujian are the most severely affected, with most stations exceeding 250 mm. Some stations in the eastern Shandong Peninsula, central and western Anhui, and northern Jiangxi also record daily precipitation extremes exceeding 350 mm.
Comparing the topography of East China (Figure 4d), it is found that several precipitation maxima (most of Fujian and Zhejiang, northern Jiangxi, central and western Anhui, and the Shandong Peninsula) correspond to significant high terrain. The precipitation centers in Fujian and Zhejiang correspond to the Daiyun Mountains, Jiufeng Mountains, and Yandang Mountains; the precipitation center in northern Jiangxi corresponds to Lushan Mountain; the precipitation center in central and western Anhui corresponds to the Dabie Mountains; and the precipitation center on the Shandong Peninsula corresponds to the Taishan and Laoshan Mountains. Almost every extreme TC rainstorm is related to terrain. The precipitation centers are mostly located at the intersections of convergence lines and the windward slopes of the terrain (Dong et al., 2011). This indicates that the unique topographical features of East China significantly influence the distribution of TC precipitation, with high terrain often leading to precipitation amplification and increased frequency of heavy rainfall events.
Analysis of factors related to TC precipitationTC path
Previous studies (Chen and Ding, 1979; Li and Chen, 2004) have consistently shown that different TC paths result in significantly different precipitation distributions. A total of 137 TCs made landfall in East China from 1960 to 2010. From the TC path map of those that made landfall in East China (figure omitted), it can be seen that most TCs originated over the ocean east of the Philippines, moving west or northwest to make landfall on the southeastern coast of China. By interpolating the path data of each TC into a 0.1° × 0.1° grid, the frequency distribution of the TC paths making landfall in East China is obtained (Figure 5). Analysis reveals that TCs making landfall in East China mainly follow four dominant paths: 1. moving northwest and then turning west after landfall (accounting for 16.1% of all landfalling TCs), 2. moving northwest (accounting for 16.8%), 3. moving northwest and then turning north after landfall (accounting for 29.9%), and 4. turning north near the coast after landfall (accounting for 19.0%). The remaining 14.6% of TCs dissipate quickly after landfall, and 3.6% have unique paths with weaker impacts on East China, which are not discussed in this paper.
Frequency distribution of TC paths that made landfall in East China from 1960 to 2010 (black arrows indicate dominant paths).
Weather map depicting a color gradient from blue to red, indicating temperature or precipitation levels across East Asia, with superimposed black arrows labeled (1) to (4). Arrows suggest movement or wind direction. The scale on the right ranges from 2 to 26.
Analyzing the average precipitation distribution for different path types of TCs (Figure 6) reveals the following: 1. TCs that turn north after landfall have the widest impact range. Fujian and Zhejiang provinces are most severely affected, with most areas receiving more than 50 mm of precipitation. The precipitation decreases gradually from the southeastern coast to the inland, with the maximum precipitation areas in northern Fujian, southern Zhejiang, and eastern coastal Zhejiang. Additionally, some secondary precipitation centers appear in northern Jiangxi, central and western Anhui, central and eastern Jiangsu, and the Shandong Peninsula. 2. The impact area of TCs that move northwest after landfall is smaller than that of the previous type, with the heavy precipitation zone significantly extending southward. The main affected areas are still Fujian and Zhejiang provinces, with weaker impacts on the northern provinces. The maximum precipitation centers are located in most of the coastal areas of Fujian and southeastern Zhejiang, with some weaker precipitation centers in western Jiangxi, central and western Anhui. 3. The precipitation distribution of TCs that move west after landfall is similar to that of those that move northwest, but the precipitation area is further south, and the magnitude of the maximum precipitation zone at the junction of Zhejiang and Fujian is smaller. 4. TCs that turn north near the coast after landfall have the weakest precipitation intensity and the smallest impact area among the four dominant paths. The strong precipitation centers are mainly distributed in the eastern coastal areas of Zhejiang, the Yangtze River Delta, and the eastern Shandong Peninsula, with weaker precipitation in other areas.
Average process precipitation distribution of TCs making landfall via different paths and schematic arrows indicating paths (black arrows) ((a) Turning north after landfall, (b) Moving northwest after landfall, (c) Moving west after landfall, (d) Turning north near the coast after landfall).
Four maps labeled (a), (b), (c), and (d) show contour plots over a region, illustrating varying color gradients from light blue to red, with a color scale from zero to one hundred. Each map has an arrow indicating different directions of movement or change across the region. The maps appear to depict meteorological or environmental data variations.TC intrinsic factors and environmental conditionsTC intrinsic factors
To further explore the impact of TC intrinsic factors on precipitation, the top ten TCs in terms of total VP (calculated based on the process precipitation amount of each individual TC) from 1960 to 2010 were selected (Table 1). Our analysis shows that 7 out of these top ten TCs followed the path of turning north after landfall, indicating that TCs with this path have a significant impact on East China. This study selects two groups of TCs with similar landfall paths (both turning north after landfall) but with significantly different total VP (Table 2) for comparative analysis to investigate the influence of intrinsic factors and environmental conditions on precipitation.
Top 10 TCs in terms of total VP (in East China) for TCs making landfall in East China from 1960 to 2010.
Rank of VP
TC name (number)
Total VP (km3)
Path type
1
Yancy (9012)
43.77
Turning west after landfall
2
Polly (9217)
41.08
Turning north after landfall
3
Amy(6217)
39.2
Turning north after landfall
4
Morakot (0908)
38.25
Turning north after landfall
5
Shirley (6010)
37.51
Turning north after landfall
6
Fung-wong (0808)
36.69
Moving northwest after landfall
7
Winnie (9711)
36.28
Turning north after landfall
8
Bilis (0010)
35.39
Turning north after landfall
9
Dot (9018)
33.31
Unique path
10
Cora(6621)
30.97
Turning north after landfall
Comparison of VP between strong and weak TCs.
Serial number
Strong TC name (number)
Total VP (km3)
Weak TC name (number)
Total VP (km3)
1
Polly (9217)
41.08
Opal (6210)
18.02
2
Amy(6217)
39.2
Kalmaegi (0807)
16.9
3
Morakot (0908)
38.25
Man-yi (0109)
12.54
4
Shirley (6010)
37.51
Ofelia (9005)
10.99
By analyzing the average cumulative precipitation (the average of the process precipitation amount of each individual TC) distribution of the two groups of TCs (Figure 7), it is found that the areas of maximum precipitation are similar for both groups, mainly distributed in the Yangtze River Delta, northeastern Fujian, and eastern coastal Zhejiang. However, the precipitation amounts differ significantly, with strong precipitation TCs having higher precipitation levels than weak precipitation TCs. In the eastern coastal areas of East China, most stations recorded more than 170 mm for strong precipitation TCs, while the corresponding areas for weak precipitation TCs recorded about 50–90 mm.
Average cumulative precipitation distribution and path map for strong (a) and weak (b) precipitation TCs.
Two maps labeled (a) and (b) showing regions with color-coded data points indicating rainfall in millimeters along a coastline. Panel (a) has a denser concentration of orange and red points, indicating higher rainfall (>170mm), while panel (b) has more green and blue points, showing less rainfall (<50mm). Black lines depict trajectories overlaid on the regions.
To comprehensively compare the intrinsic factors of strong and weak TCs, the parameters of TC movement speed, maximum wind speed, minimum pressure, and maximum daily precipitation were analyzed (Figure 8). The results indicate that, in terms of movement speed, strong precipitation TCs have an average movement speed of only 21.6 m/s, slower than that of weak precipitation TCs. For maximum wind speed, strong precipitation TCs have an average maximum wind speed of 27.5 m/s, higher than the 22.8 m/s of weak precipitation TCs. Regarding maximum daily precipitation, strong precipitation TCs have an average maximum daily precipitation of 314.93 mm, higher than the 230.43 mm of weak precipitation TCs. In terms of average minimum pressure, strong precipitation TCs have a slightly lower average minimum pressure of 978.24 hPa compared with the minimum pressure of weak precipitation TCs. Overall, strong precipitation TCs exhibit characteristics of slower movement speed, stronger maximum wind speed, higher maximum daily precipitation, and lower minimum pressure compared with weak precipitation TCs.
Comparison of factors between strong and weak precipitation TCs.
Bar graph comparing strong and weak tropical cyclones in four categories: moving speed, maximum wind speed, minimum pressure, and maximum daily precipitation. Strong cyclones generally have higher wind speeds and precipitation, but lower minimum pressure than weak cyclones, except in moving speed where weak cyclones are faster.Environmental conditions
Following Gray (1979) and Li et al. (2005) dynamic composite method, this study conducts composite analyses of the environmental conditions surrounding different TCs to compare and examine the environmental characteristics of strong and weak TCs. The NCEP reanalysis data (with a temporal resolution of 4 times/day, horizontal resolution of 2.5° × 2.5°, and 17 vertical layers) is used for the dynamic composite analysis of strong and weak rainfall TCs. The composite field area is a square range with the TC center at its center and extending ±40° in latitude and longitude from the TC center. The analysis primarily discusses the composite fields of strong and weak rainfall TCs at the time of landfall, 24 h post landfall, and 48 h post landfall. The TC path data is selected from the best track dataset provided by the Shanghai TC Institute, and the nearest NCEP reanalysis grid point data is used for the landfall time and post landfall positions in the composite analysis.
First, we examined the characteristics of the upper-level circulation. Through the composite 500 hPa circulation fields at different times for strong and weak TCs (Figures 9a,b), we found that the common feature of strong and weak TC circulation patterns is the presence of a stable high-pressure system on the east side of the TC, namely, the subtropical high. This is also an important factor causing TCs to turn northward after landfall. Additionally, there is a deeper westerly trough in the mid-high latitude region to the north of strong rainfall TCs. The interaction between the TC and the upper-level trough of different intensities, with the deeper trough bringing strong cold advection, positive vorticity advection, and strong upper-level divergence ahead of the trough, is conducive to the maintenance and extratropical transition of the TC (Li et al., 2006). In contrast, weak rainfall TCs are often located in the northwestern descending airflow ahead of the high-pressure ridge (Figures 10a,b), with weaker instability energy, which is unfavorable for the redevelopment and strengthening of the TC after turning northward.
Composite fields 24 h after landfall: (a,c,e) Strong precipitation TC; (b,d,f) Weak precipitation TC. (a,b) 500 hPa geopotential height (shaded, unit: 10 gpm) and wind vectors (unit: m/s); (c,d) 1,000 hPa temperature (shaded, unit: °C) and 850 hPa wind vectors (unit: m/s); (e,f) 925 hPa moisture flux (shaded, unit: g/(hPa·cm·s)) and wind vectors (unit: m/s).
Six meteorological contour maps labeled (a) to (f), display varying pressure levels and wind vectors. Maps (a) and (b) show high pressure regions in red with values 580 to 588. Maps (c) and (d) also display pressure variations but with a smoother gradient. Maps (e) and (f) illustrate wind patterns with color gradients indicating pressure from red to blue, reflecting different intensities across coordinates 0 to 30 on both axes.
Next, we analyzed the temperature field. Higher sea surface temperatures provide favorable conditions for the formation, maintenance, and development of TCs (Zhu et al., 2007). Comparing the 1,000 hPa temperature fields of the two groups of TCs at the time of landfall, 24 h post landfall (Figures 9c,d), and 48 h post landfall (Figures 10c,d), we found that at the time of landfall, the center of strong rainfall TCs is in the range of 27 °C–29 °C, and the main body of the TC is far from the low temperature area to the north, with only weak cold air affecting the periphery of the TC. For weak rainfall TCs, the <23 °C area to the north of the center shows a trend of encroachment towards the TC; 24 h post landfall, the cold air to the north of the strong rainfall TC slightly presses southward, while for weak rainfall TCs, the cold air to the north significantly presses southward, approaching closer to the TC center; 48 h post landfall, the center of the strong TC remains in a 25 °C–27 °C environment, with cold air affecting the peripheral circulation of the TC, maintaining the warm core structure. In contrast, the center of weak rainfall TCs is invaded by cold air, disrupting the warm core structure.
Composite fields 48 h after landfall: (a,c,e) Strong precipitation TC; (b,d,f) Weak precipitation TC. (a,b) 500 hPa geopotential height (shaded, unit: 10 gpm) and wind vectors (unit: m/s); (c,d) 1,000 hPa temperature (shaded, unit: °C) and 850 hPa wind vectors (unit: m/s); (e,f) 925 hPa moisture flux (shaded, unit: g/(hPa·cm·s)) and wind vectors (unit: m/s).
Six-panel contour plots labeled (a) to (f) display various meteorological variables with color gradients and vector fields. Panels (a) and (b) show contour intervals of geopotential height in decameters, ranging from 552 to 596. Panels (c) and (d) depict temperature contours in degrees Celsius, with values between 7 and 31. Panels (e) and (f) represent vorticity fields with values from 3 to 23. Each plot includes vector arrows indicating wind direction and intensity. Color bars on the right define the range for each variable, and the reference vector at the bottom of each panel provides scale.
Finally, we analyzed the moisture conditions of the TCs. Comparing the 925 hPa composite moisture flux and wind vector fields of strong and weak TCs (Figures 9e,f), it is found that the moisture channel in the southwestern quadrant of strong rainfall TCs remains connected to the main body of the TC for a long time, only breaking 48 h post landfall (Figures 10e,f). However, for weak rainfall TCs, the moisture channel in the southwestern quadrant breaks as early as 24 h post landfall. Different moisture transport conditions post landfall are the primary reasons for the significant difference in rainfall intensity between the two groups of TCs.
In summary, strong rainfall TCs are often associated with an upper-level trough to the north, with their warm core structure and moisture channel maintained for a long time after landfall; whereas weak rainfall TCs are often located behind an upper-level trough, with their warm core structure disrupted by cold air and their moisture channel breaking early.
Conclusion
Based on the TC precipitation separation data using the OSAT method, this study statistically analyzes the precipitation characteristics of TCs that made landfall in the East China from 1960 to 2010 and investigates the causes of this precipitation. The following conclusions are drawn:
In terms of interannual variations, the total volume of precipitation from TCs that made landfall in East China shows a relatively stable trend, while the maximum daily precipitation at individual stations is gradually increasing. Both the frequency of TCs and the frequency of TCs bringing extreme precipitation (≥250 mm) exhibit a “single peak” monthly distribution, peaking in August.
Regarding the spatial distribution of precipitation, both the frequency of heavy rain (≥50 mm) and torrential rain (≥100 mm) recorded at stations have their extreme value areas primarily distributed in the southeastern coastal regions, with a gradual weakening trend from the coast to inland areas. Major high-precipitation areas include most of Fujian and Zhejiang, northern Jiangxi, central and western Anhui, and the Shandong Peninsula.
When considering the factors influencing precipitation distribution, the key factors include TC path, topographic effects, TC intrinsic factors, and environmental conditions. Different TC paths often lead to different precipitation distributions. There are four main favorable paths for TCs making landfall in East China, with the “northward after landfall” type having the most significant impact on precipitation in the region. The topographical features of East China significantly affect the precipitation distribution of landfalling TCs, with high terrain often leading to increased precipitation and higher frequencies of heavy rainfall. Strong rainfall TCs, compared with weak ones, have slower movement speeds, stronger maximum wind speeds, and lower minimum pressure. Additionally, strong rainfall TCs are often associated with an upper-level trough to the north, maintaining their warm core structure and moisture channel for a longer time post landfall. In contrast, weak rainfall TCs are usually located behind an upper-level trough, with their warm core structure often disrupted by cold air and their moisture channel breaking earlier.
Data availability statement
Confidentiality regulations: the original typhoon precipitation data and associated meteorological observations contain sensitive information protected under China’s state confidentiality laws and regulations. Data access: controlled access to de-identified or processed data may be granted for academic collaboration upon formal request to the Xiamen Key Laboratory of Straits Meteorology, pending approval by relevant authorities. Derivative data: non-sensitive analytical results (e.g., statistical summaries, processed spatial distributions) are available in the manuscript and supplementary materials. For inquiries regarding conditional data access, please contact the corresponding author (343625321@qq.com) with a detailed research proposal and institutional affiliation.
Author contributions
LY: Writing – original draft, Writing – review and editing, Conceptualization, Methodology, Software, Validation. JZ: Data curation, Methodology, Writing – review and editing. ZW: Software, Validation, Writing – review and editing. ZS: Conceptualization, Software, Writing – review and editing.
Acknowledgements
We would like to express our gratitude to researcher Ren Fumin and his research team for providing the TC precipitation separation data and technical guidance.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Generative AI was used in the creation of this manuscript.
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.
Publisher’s note
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.
Edited by:Feifei Shen, Nanjing University of Information Science and Technology, China
Reviewed by:Qinglan Li, Chinese Academy of Sciences (CAS), China
Jiafeng Zheng, Chengdu University of Information Technology, China
ReferencesChenL.DingY. (1979). Introduction to Western Pacific typhoons. Beijing: Science Press.ChengZ.ChenL.LiY. (2012). Interaction between landfalling tropical cyclone and summer monsoon with influences on torrential rain. J. Appl. Meteorological Sci.23 (6), 660–671. 10.11898/1001-7313.20120603ChengZ.ChenL.LiuY.PengT. (2007). The spatial and temporal characteristics of tropical cyclone-induced rainfall in China during 1960-2003. J. Appl. Meteorological Sci.18 (4), 427–434. 10.11898/1001-7313.20070402DongM. (2010). Mechanism of precipitation enhancement in landfalling tropical cyclones. Beijing, China: Chinese Academy of Meteorological Sciences & Nanjing University of Information Science & Technology. PhD Dissertation.DongM.ChenL.ChengZ.LiY. (2011). Numerical study of topography effect on rainfall reinforcement associated with tropical cyclone ‘talim. Plateau Meteorol.30 (3), 700–710.GrayW. M. (1979). Recent advance in tropical cyclone research from rawinsonde analysis. Geneva, Switzerland: World Meteorological Organization.LiY.ChenL. S.WangJ. Z. (2005). Diagnostic study of the sustaining and decaying of tropical cyclones after landfall. Chin. J. Atmos. Sci.29 (3), 482–490. 10.1007/s10409-004-0010-xLiY.ChenL. (2004). Statistical characteristics of tropical cyclone making landfalls on China. J. Trop. Meteorology20 (1), 14–22. 10.3969/j.issn.1004-4965.2004.01.002LinY.LiuM.LiuA.HuangM. (2007). Causation analysis of mesoscale heavy rain triggered by typhoon longwang. Meteorology33 (2), 22–27. 10.7519/j.issn.1000-0526.2007.02.004RenF.GleasonB.EasterlingD. R. (2001). A numerical technique for partitioning cyclone tropical precipitation. J. Trop. Meteorology17 (3), 308–313. 10.3969/j.issn.1004-4965.2001.03.015RenF. M.GleasonB.EasterlingD. R. (2002). Typhoon impacts on china’s precipitation during 1957-1996. Adv. Atmos. Sci.19 (5), 943–952. 10.1007/s00376-002-0057-1RenF.WangY.WangX.LiW. (2007). Estimating tropical cyclone precipitation from station observations. Adv. Atmos. Sci.24 (4), 700–711. 10.1007/s00376-007-0700-yYingL.ChenL.XiaotuL. (2006). Numerical study on impacts of upper-level westerly trough on the extratropical transition process of typhoon winnie (1997). Acta Meteorol. Sin.64 (5), 552–563. 10.3321/j.issn:0577-6619.2006.05.002YuZ.GaoS.RenH. (2007). A numerical study of the severe heavy rainfall associated with the typhoon haitang (0505). Acta Meteorol. Sin.65 (6), 864–876. 10.11676/qxxb2007.081YuanJ.DingZ.WangL. (2011). A statistical study and composite analysis on the characteristics of the extratropical transition of landfall typhoons during 1949-2007. J. Trop. Meteorology27 (4), 529–541. 10.3969/j.issn.1004-4965.2011.04.010ZhangY.XuH. (1996). Disasters and reflections of typhoon 9417 in Wenzhou. Zhejiang Hydraulic Sci. Technol.2 (12), 34–36.ZhuQ.LinJ.ShouS.TangD. (2007). Principles and methods of synoptic meteorology. 4th Edition. Beijing: China Meteorological Press.