Individual Influence of Climate Variability Indices on Annual Maximum Precipitation Across the Global Scale

Studying the influence of climate variability indices on extreme precipitation will help to understand the variability of extreme precipitation. However, the influence of climate indices on extreme precipitation over the world has received little attention. In this work, a stationary generalized extreme value (GEV) model and nonstationary GEV models on the annual maximum 1-day precipitation (Rx1day) at the global scale are developed. The Bayesian framework is adopted in this study. Four climate variability indices such as the El Nino-Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) are used independently as covariates. The results showed that nonstationary GEV models are preferred over the stationary GEV model based on Deviance Information Criterion (DIC) and the significant covariates for a large number of grid cells which indicates that the influence of climate index is not a negligible component in the GEV model. In addition, the positive and negative influences of the covariates are analyzed. At the global, the effect of ENSO on the location parameter is greater than the effect of the other covariates, indicating that ENSO has a strong influence on extreme precipitation in large parts of the world.