Benefits of the simplified MEV for analyzing hourly precipitation extremes in a changing climate

Predicting the likelihood of extreme hourly rainfall events is crucial in mitigating risks associated with flash floods and related hazards. Previous research shows that, for limited sample sizes, the simplified Metastatistical Extreme Value (sMEV) distribution can significantly reduce the associated uncertainty in rainfall return levels compared to the more commonly used General Extreme Value (GEV) distribution. Recent research also highlights the possibility to analyze the effects of climate change using the non-stationary versions of both distributions. Thus, we investigate the sMEV and GEV distributions for hourly precipitation obtained from 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 historical, near-future and far-future periods. This comparison is novel in combining a large spatial and temporal extent with a high spatial and temporal resolution. The results show that, in the current climate, the GEV method estimates on average higher return levels than the sMEV for hourly extremes. However, this difference reverses with further global warming. The results also reveal, that the sMEV outperforms the GEV in the sensitivity to small samples across almost all return periods regardless of the length of observational records. In addition, there is a north-south gradient in the return level difference, the uncertainty difference and crucially the adequacy of the sMEV left-censoring threshold. In addition, we investigate non-stationary versions of the sMEV and GEV under climate change. The chosen non-stationary sMEV shows more promise in describing the change in return levels compared to the non-stationary GEV. However, both non-stationary versions analyzed lack complexity and should be used carefully when projecting future rainfall extremes.