Flood events are difficult to characterize if available observation records are shorter than the recurrence intervals, and the non-stationarity of the climate adds additional uncertainty. In this study, we use a hydrological model coupled with a stochastic weather generator to simulate the summer flood regime in two mountainous catchments located in China and Switzerland. The models are set up with hourly data from only 10–20 years of observations but are successfully validated against 30–40-year long records of flood frequencies and magnitudes. To assess the climate change impacts on flood frequencies, we re-calibrate the weather generator with the climate statistics for 2021–2050 obtained from ensembles of bias-corrected regional climate models. Across all assessed return periods (10–100 years) and two emission scenarios, nearly all model chains indicate an intensification of flood extremes. According to the ensemble averages, the potential flood magnitudes increase by more than 30% in both catchments. The unambiguousness of the results is remarkable and can be explained by three factors rarely combined in previous studies: reduced statistical uncertainty due to a stochastic modelling approach, hourly time steps and the focus on headwater catchments where local topography and convective storms are causing runoff extremes within a confined area.