Rainfall Regionalization for Mainland China Based on Storm Characteristics

ABSTRACT This study develops a high‐resolution rainfall regionalization for mainland China based on sub‐daily storm characteristics, addressing key limitations of previous classifications that relied on daily or coarser precipitation data and limited rainfall metrics. Using hourly records from 2275 meteorological stations between 1971 and 2020, over 2.31 million rainfall events (≥ 10 mm) were identified and categorised into five event types (A–E) based on event rainfall depth (total rainfall accumulation, mm), event duration (total hours), and peak rainfall intensity (maximum hourly intensity). Stations were then clustered into six groups and aggregated into eight rainfall zones, integrating both event‐type composition and topographic context. Pairwise correlation analysis reveals that 99.1% and 99.6% of stations exhibit significant positive correlations between event depth and duration, and between event depth and peak intensity, respectively—indicating that higher rainfall depths generally result from either longer durations or stronger intensities. In contrast, 92.8% of stations show a significant negative correlation between duration and peak intensity, suggesting a typical trade‐off whereby shorter events tend to be more intense. Spatially, mean event rainfall depth decreases from southeast to northwest. Mean durations display a “sandwich‐like” pattern across central and eastern China, with the longest events (> 21 h) occurring in the Qinba Mountains and Weihe Plain, Middle‐Lower Yangtze Plain and Northern Southeast Hills, Yunnan‐Guizhou Plateau, and southeastern Tibetan Plateau. Shorter durations dominate both southern coastal and northern inland regions. Peak intensity shows an inverse pattern, with the highest values (> 12 mm·h −1 ) along the Southern Southeast Hills and North China Plain and elevated values (10–12 mm·h −1 ) in the western Sichuan Basin. The station classification demonstrates that stations with similar event‐scale rainfall characteristics—despite wide geographic and climatic differences—can be grouped into the same station types, emphasising the role of common physical drivers such as moisture transport and topography. The use of event‐type composition as a key classification metric proves effective in distinguishing station‐level rainfall regimes. Moreover, the resulting rainfall zones align closely with major weather systems and physiographic boundaries, affirming the physical‐geographic basis of the regionalization. This study offers a transferable, event‐based framework for hydroclimatic classification, providing valuable insights for water resource management, flood risk assessment, and climate adaptation planning.

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Publication Details

Journal
International Journal of Climatology
Published
2026-03-26
DOI
https://doi.org/10.1002/joc.70352
Primary Topic
Climate variability and models
Type
article
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article

Rainfall Regionalization for Mainland China Based on Storm Characteristics

Yao Xiao, Bofu Yu, Deliang Chen, Shuiqing Yin et al.
International Journal of Climatology
Climate variability and models
article

Rainfall Regionalization for Mainland China Based on Storm Characteristics

Yao Xiao, Bofu Yu, Deliang Chen, Shuiqing Yin, Yuxin Zhang
article en

Abstract

ABSTRACT This study develops a high‐resolution rainfall regionalization for mainland China based on sub‐daily storm characteristics, addressing key limitations of previous classifications that relied on daily or coarser precipitation data and limited rainfall metrics. Using hourly records from 2275 meteorological stations between 1971 and 2020, over 2.31 million rainfall events (≥ 10 mm) were identified and categorised into five event types (A–E) based on event rainfall depth (total rainfall accumulation, mm), event duration (total hours), and peak rainfall intensity (maximum hourly intensity). Stations were then clustered into six groups and aggregated into eight rainfall zones, integrating both event‐type composition and topographic context. Pairwise correlation analysis reveals that 99.1% and 99.6% of stations exhibit significant positive correlations between event depth and duration, and between event depth and peak intensity, respectively—indicating that higher rainfall depths generally result from either longer durations or stronger intensities. In contrast, 92.8% of stations show a significant negative correlation between duration and peak intensity, suggesting a typical trade‐off whereby shorter events tend to be more intense. Spatially, mean event rainfall depth decreases from southeast to northwest. Mean durations display a “sandwich‐like” pattern across central and eastern China, with the longest events (> 21 h) occurring in the Qinba Mountains and Weihe Plain, Middle‐Lower Yangtze Plain and Northern Southeast Hills, Yunnan‐Guizhou Plateau, and southeastern Tibetan Plateau. Shorter durations dominate both southern coastal and northern inland regions. Peak intensity shows an inverse pattern, with the highest values (> 12 mm·h −1 ) along the Southern Southeast Hills and North China Plain and elevated values (10–12 mm·h −1 ) in the western Sichuan Basin. The station classification demonstrates that stations with similar event‐scale rainfall characteristics—despite wide geographic and climatic differences—can be grouped into the same station types, emphasising the role of common physical drivers such as moisture transport and topography. The use of event‐type composition as a key classification metric proves effective in distinguishing station‐level rainfall regimes. Moreover, the resulting rainfall zones align closely with major weather systems and physiographic boundaries, affirming the physical‐geographic basis of the regionalization. This study offers a transferable, event‐based framework for hydroclimatic classification, providing valuable insights for water resource management, flood risk assessment, and climate adaptation planning.

International Journal of Climatology
Griffith University (AU), Beijing Normal University (CN), Tsinghua University (CN)
Life below water
Openalex Percentile: Top 5%
Climate variability and models
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