This study utilizes historical monitoring data from the Xu Fan small watershed spanning 1962 to 2021 and employs the K-means clustering algorithm to classify extreme rainfall events into three distinct categories: short-duration high-intensity rainfall, sustained moderate-intensity rainfall, and long-duration heavy rainfall. Through the application of the Random Forest model, key factors influencing flood characteristics are identified, including total rainfall, maximum rainfall intensity, the timing of maximum intensity, and rainfall duration. The comparative analysis of data before and after 1990 highlights that climate change has led to increased maximum rainfall intensity, reduced rainfall duration, and shifts in the temporal distribution of rainfall, thereby exerting a significant influence on the flood generation process.

Climate change increases the intensity and frequency of extreme rainfall. Heavy rain is one of the main input sources for the complex water resources system in the watershed. Understanding its regional spatial correlation is of vital importance for promoting sustainable disaster management in the watershed. The Qiantang River Basin is a significant ecological and economic area in the Yangtze River Delta, yet systematic research on its multi-regional rainstorm-dependent structure remains insufficient. In this study, hourly rainfall data of the basin from 1950 to 2024 were used to construct marginal functions by using the peaks-over-threshold and the generalized Pareto distribution, and a mixed Copula model was established to describe the dependence structure of multi-regional extreme rainfall events. The model has been tested by RMSE and Cramér–von Mises statistics and shows reliable performance. The study reveals that the basin has a “double cluster” spatial pattern: the internal conditions of northern clusters (Hangzhou–Shaoxing) and southern clusters (Jinhua–Lishui–Quzhou) showed a strong dependence. On the contrary, under cluster conditions with low inter-regional dependence, all high-probability combinations occurred within the clusters, not outside them. This finding provides quantitative support for optimizing trans-regional emergency response, improving flood control resilience, and realizing precise allocation of resources, and is of great significance for promoting sustainable watershed governance.

Climate change exerts heterogeneous impacts on regional flood extremes. This study focuses on the XF watershed (dominated by Meiyu) and HJT watershed (dominated by typhoons) in Zhejiang Province from 1964 to 2021. The Annual Maximum Flood (AMF) method and Peaks-Over-Threshold (POT) method were employed to analyze the differences in extreme flood characteristics before and after 1990. The results indicate that the Meiyu-dominated XF watershed shows stability: the 100-year design flood decreased by 2.3% using the AMF method and increased by 3.9% using the POT method. In contrast, the typhoon-dominated HJT watershed exhibits a decoupled response of “decreased frequency - amplified intensity”: the annual exceedance rate decreased from 1.885 to 1.808 events per year (a 4.1% reduction), while the Generalized Pareto Distribution (GPD) scale parameter increased by 42.2%. There is a significant discrepancy in the estimated values between the two methods in the HJT watershed (AMF: +64.2%; POT: +7.0%), highlighting the vulnerability of the AMF method in highly variable systems. This study reveals the differentiated response patterns of floods to climate change under different rainfall mechanisms, providing a scientific basis for regional flood control planning and risk management.

Methods the annual maximum flood (AMF) and the peak over threshold (POT) methods, and applies extreme value theory to analyze the flood data. In the AMF method, the statistical characteristics of flood peaks are described by fitting a Gumbel distribution; in the POT method, the tail behavior of flood events exceeding a set threshold is modeled using the Generalized Pareto Distribution (GPD). Additionally, rainfall intensity, duration, and frequency are statistically analyzed, and the Mann-Kendall non-parametric trend test, which accounts for autocorrelation effects, is used to reveal significant changes in the spatiotemporal distribution and extreme value characteristics of floods. The results show that, since 1990, the peak flood month in the XF watershed has shifted from July to June, while flood events in August have significantly increased in the HJT watershed. Furthermore, the temperature has shown a significant upward trend, consistent with the theoretical expectation of increased atmospheric water vapor and intensified extreme rainfall. Although a single trend test is insufficient to directly prove the causal effect of climate change, the combined changes in extreme value distribution parameters provide solid statistical evidence for the potential impact of climate change on flood extremes and their spatiotemporal distribution. The findings of this study offer scientific support for regional flood management, infrastructure design, and future climate-adaptive flood risk management.