Event-based evaluation of IMERG for extreme precipitation in China: Insights from an error decomposition approach

作者：Yunfei Tian, Hao Guo, Chunrui Guo, Baofu Li, Ying Cao, Liangliang Jiang, Anming Bao, Philippe De Maeyer · 发表于：Journal of Hydrology Regional Studies · 年份：2025 · DOI：10.1016/j.ejrh.2025.102657 · 被引用次数：1 · 研究领域：Precipitation Measurement and Analysis、Meteorological Phenomena and Simulations、Climate variability and models

Study region Chinese mainland Study focus Using hourly precipitation data from over 2100 stations across China, this study applies an event-based error decomposition method to systematically evaluate IMERG’s performance in capturing extreme precipitation processes and investigates the influence of different climate regions, seasons, and topographic conditions on error components. New hydrological insights for the region Precipitation occurs in discrete “events” with defined start time, duration, and intensity. Traditional evaluation methods oversimplify the complexity of precipitation dynamics. Therefore, assessing the accuracy of IMERG in capturing extreme precipitation from an event-based perspective is essential for Chinese mainland. Results reveal that IMERG generally overestimates event duration (ED), total precipitation (Esum), and frequency (EF), while underestimating maximum precipitation (Emax). The Total bias is predominantly positive, particularly in humid and semi-humid areas, where False bias dominates. False-Event and Miss-Event significantly contribute to the cumulative extreme precipitation error. IMERG often detects events prematurely, and accuracy is affected by geography and event intensity. Across four climate regions, False-Event is the dominant error source. Seasonal analysis shows False-Event and Miss-Event together contribute 65.3 %–72.4 % of total error. Although contributions vary across elevations, False-Event remains the largest, while Hit Negative...