Dynamic Identification of Tropical Cyclone Precipitation Peaks and Regional Response in Eastern China (2004–2023)Dynamic Identification of Tropical Cyclone Precipitation Peaks and Regional Response in Eastern China (2004–2023)

Tropical cyclone (TC) precipitation is one of the main disaster-causing factors in the coastal regions of eastern China. To address key issues in previous studies, including insufficient quantification of precipitation peak lag effects, reliance on single-indicator sensitivity assessments, and a lack of focus on inland areas, this study develops a dynamic identification and multi-indicator comprehensive assessment framework. This framework utilizes hourly station precipitation data and TC best-track datasets (2004–2023) from the China Meteorological Administration. We quantify precipitation contribution rates during different TC landfall stages and the time lag of precipitation peaks (both daily and hourly) for various provinces. Extreme precipitation thresholds are defined using the 95th percentile method. A comprehensive sensitivity index (S) is constructed by coupling the coefficient of variation of daily maximum precipitation intensity with the change rate of precipitation frequency via the entropy weight method. Finally, the spatial clustering of S is analyzed using Local Moran’s I. The results show that TC precipitation mainly concentrates on the day of and the day following TC landfall, accounting for 58.38% of the total precipitation, although there is a significant coastal–inland gradient in peak precipitation timing. Coastal provinces (Hainan, Zhejiang, and Fujian) respond rapidly, with daily precipitation peaks lagging less than 0.3 days and hourly peaks occurring within 3–6 hours after TC landfall. While for inland provinces (e.g., Heilongjiang), lag times can exceed 3 days. Extreme precipitation thresholds are highest along the southeastern coast (Hainan: 121.5 mm) but are abnormally high (>200 mm) at some stations in northern inland provinces (Beijing: 127.5 mm). The comprehensive sensitivity index identifies the North China Plain (Shandong, Hebei, and Beijing) and the middle Yangtze River Basin (Chongqing) as areas of high sensitivity (mean S: 0.45–0.46), characterized by high interannual variability in precipitation intensity and a significant increasing trend in precipitation frequency. Spatial correlation analysis further reveals that the North China Plain forms large contiguous high-high clusters (hot spots), where the southeastern coast features stable low-low clusters (cold spots). This study is the first to finely characterize the precipitation peak lag gradient from the coast to inland in eastern China and to identify the North China Plain as an emerging high-sensitivity risk area for TC precipitation. The findings provide scientific support for understanding the regional response patterns of TC precipitation under climate change and for formulating differentiated disaster prevention and mitigation strategies..