Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence

Extreme rainfall can be affected by various climatic factors such as the large-scale climate patterns (LCPs). Understanding changing LCPs improve accuracy of extreme prediction. This study explores variation trend in middle and lower reaches Yangtze River Basin (MLRYRB) telecorrelation with four LCPs, namely WPSHI (Western Pacific Subtropical High Index), EAMI (East Asia Monsoon ENSO (El Niño-Southern Oscillation) PDO (Pacific Decadal Oscillation), through modified Mann-Kendall (MMK) analysis, Pearson correlation coefficient, wavelet coherence analysis (WTC) improved partial (PWC). Previous studies have ignored interdependence between these indices when analyzing their effects on precipitation. introduces PWC, which remove correlations them reveal influence a single LCP. The results show that: 1) MLRYRB has an obvious increasing significant LCPs; 2) cyclical relationship rainfall, significantly intergenerational 3) PWC accurately is main influencing factor 1-year cycle, while 5-year cycle. always coupling or EAMI.