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 evaluate the performance of 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. To our knowledge, this is the first application of the sMEV distribution to time series from a convection-permitting-model. The results show that the sMEV outperforms the GEV in terms of uncertainty 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. 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 under climate change. However, both non-stationary versions analyzed lack complexity and should be used carefully when projecting future rainfall extremes.