Clarifying the relationship between annual maximum daily precipitation and climate variables by wavelet analysis

Abstract

Analyzing the temporal hydrological response of regional precipitation to climate variables is critical for improving precipitation prediction and understanding the underlying mechanism. We study the potential relationship between the annual maximum daily precipitation (RX1day) in Guangdong Province and 21 climate variables, including the length of the day of the earth's rotation (LOD), Niño 3.4, and Pacific Decadal Oscillation (PDO). Wavelet coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) are used to identify possible individual, independent, and coupled relationships between the RX1day and climate variables. The analysis results show that Tropical Southern Atlantic Index (TSA), Niño 3.4, Southern Oscillation Index (SOI), Western Pacific Index (WP), Tripole Index for the Interdecadal Pacific Oscillation (TPI), North Pacific pattern (NP), Atlantic Multidecadal Oscillation (AMO), and Tropical Northern Atlantic Sea Surface Temperatures Index (TNA) are important driving forces influencing RX1day in Guangdong. After excluding the influence of Niño 3.4 and PDO, PWC accurately explains the influence of a single climate variable on RX1day. A single climate variable is insufficient to describe the influence on RX1day, but MWC accurately reflects the combined influence of different climate variables on RX1day. Our work emphasizes the use of PWC and MWC to reveal the independent and combined influences of climate variables on RX1day on different time scales. This study provides new insights into selecting the optimum predictive factors of RX1day.