Xiao et al. (2026) Rainfall Regionalization for Mainland China Based on Storm Characteristics

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-03-26
• Authors: Yao Xiao, Shuiqing Yin, Yuxin Zhang, Bofu Yu, Deliang Chen
• DOI: 10.1002/joc.70352

Research Groups

[Information not available in the provided abstract.]

Short Summary

This study develops a high-resolution rainfall regionalization for mainland China using sub-daily storm characteristics from hourly station data. It identifies five event types and clusters stations into six groups, aggregated into eight rainfall zones, revealing distinct spatial patterns of rainfall depth, duration, and intensity, and their correlation with topography and weather systems.

Objective

• To develop a high-resolution rainfall regionalization for mainland China based on sub-daily storm characteristics, addressing limitations of previous classifications that relied on daily or coarser precipitation data and limited rainfall metrics.

Study Configuration

• Spatial Scale: Mainland China, utilizing hourly records from 2275 meteorological stations.
• Temporal Scale: 50 years (1971-2020), focusing on sub-daily storm characteristics derived from hourly rainfall records.

Methodology and Data

• Models used: Classification and clustering algorithms (not explicitly named, but implied by "stations were then clustered into six groups and aggregated into eight rainfall zones").
• Data sources: Hourly rainfall records from 2275 meteorological stations across mainland China.

Main Results

• Over 2.31 million rainfall events (≥ 10 mm) were identified and categorized into five event types (A–E) based on event rainfall depth, duration, and peak rainfall intensity.
• Stations were clustered into six groups and aggregated into eight rainfall zones, integrating event-type composition and topographic context.
• Pairwise correlation analysis showed:
  • 99.1% of stations exhibit significant positive correlations between event depth and duration.
  • 99.6% of stations exhibit significant positive correlations between event depth and peak intensity.
  • 92.8% of stations show a significant negative correlation between duration and peak intensity, indicating a typical trade-off.
• 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 hours) 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 can be grouped together despite wide geographic and climatic differences, emphasizing the role of common physical drivers such as moisture transport and topography.
• The resulting rainfall zones align closely with major weather systems and physiographic boundaries, affirming the physical-geographic basis of the regionalization.

Contributions

• Develops a novel high-resolution rainfall regionalization for mainland China based on sub-daily storm characteristics, overcoming limitations of previous classifications that used daily or coarser data and limited rainfall metrics.
• Introduces event-type composition as a key and effective classification metric for distinguishing station-level rainfall regimes.
• Provides a transferable, event-based framework for hydroclimatic classification.
• Offers valuable insights for water resource management, flood risk assessment, and climate adaptation planning.

Funding

[Information not available in the provided abstract.]

Citation

@article{Xiao2026Rainfall,
  author = {Xiao, Yao and Yin, Shuiqing and Zhang, Yuxin and Yu, Bofu and Chen, Deliang},
  title = {Rainfall Regionalization for Mainland China Based on Storm Characteristics},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70352},
  url = {https://doi.org/10.1002/joc.70352}
}