Assessment of Teleconnections of Extreme Precipitation with Large-Scale Climate Indices: A Case Study of the Zishui River Basin, China

With global climate change, the frequency of extreme precipitation events in the Zishui River Basin (ZRB) is increasing, presenting significant challenges for water resource management. This study focuses on analyzing the evolution of extreme precipitation trends during the flood season from 1979 to 2018 and investigating their remote correlations with 18 large-scale climate indicators (LCIs) using three-dimensional (3D) Vine Copula. The results indicate a significant downward trend in the sustained wetness index (CWD) during the flood season, while trends in other extreme precipitation indices (EPIs) are not significant. Notably, a significant correlation exists between Maximum Precipitation for One Day (RX1day) and the Pacific Decadal Oscillation (PDO), Pacific North American pattern (PNO), and Sustained Drought Index (CDD), as well as between Atlantic Multi-decadal Oscillation (AMO) and PDO. Excluding the optimal marginal distribution of PDO, which follows a Laplace distribution, the optimal marginal distributions of the other indices conform to a Beta distribution. The C-Vine Copula function was employed to establish the functional relationships among RX1day, PDO, PNO, CDD, and AMO, allowing for an analysis of the impact of model fitting on EPIs under different LCI scenarios. The findings of this study are significant for the ZRB and other inland monsoon climate zones, providing a scientific foundation for addressing climate extremes and enhancing flood monitoring and prediction capabilities in the region.