Exploring Hourly Rainfall Extremes in a Changing Climate

Previous research shows that for limited sample sizes applying the simplified metastatistical extreme value (sMEV) distribution instead of the more commonly used general extreme value (GEV) distribution can significantly reduce the associated uncertainty in rainfall return levels. Recent literature has also highlighted the possibility to analyze the effects of climate change using the non-stationary version of the sMEV distribution. Thus, the objective of this study is to test the performance of the sMEV and GEV for hourly precipitation using a convection-permitting regional climate model. The global climate model MIROC5 is employed to drive the regional climate model COSMO over the greater Germany area for the past, near future, and distant future. It is set up at a high temporal and spatial resolution allowing it to explicitly resolve deep convection, which is important when assessing extreme hourly precipitation. No comparable time series from a convection-permitting model has previously been analyzed using the sMEV distribution. The results show that the sMEV performs much better than the GEV in terms of the uncertainty for almost all return periods regardless of the observational years available. In addition, there is a north-south gradient in the return level difference, the uncertainty difference and the adequacy of the left-censoring threshold chosen for the sMEV. Investigating non-stationary versions of the sMEV and GEV shows that the non-stationary sMEV is more suitable to describing the change in return levels. However, both implemented versions of the non-stationary distributions are limited by the complexity of the temperature dependency. Therefore, we recommend a careful application for the prediction of return levels under higher temperatures.