Modeling Return Levels of Non-Stationary Rainfall Extremes Due to Climate Change

Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We applied stationary and non-stationary Generalized Extreme Value (GEV) models within a Bayesian framework to estimate return levels for rainfall extremes, along with 90% confidence intervals. Our findings indicate that non-stationary models are not prominently evident based on AIC at most stations; however, non-stationary Generalized Extreme Value (GEV) models outperform stationary models based on RMSE and NSE evaluation criteria that sufficiently capture variations in extremes. Furthermore, most observed changes in extreme events exhibit a non-stationary pattern. Non-stationary analysis indicates that the frequency and severity of rainfall extremes have shown both increasing and decreasing trends, characterized by inconsistent spatial patterns.