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The assessment of return periods of extreme hydrological events often relies on statistical analysis using generalized extreme value (GEV) distributions. Here we compare the traditional GEV approach with a novel large ensemble approach to determine the added value of a direct, empirical distribution-based estimate of extreme hydrological events. Using the global climate and hydrological models EC-Earth and PCR-GLOBWB, we simulate 2,000 years of global hydrology for a present-day and 2 degrees C warmer climate. We show that the GEV method has inherent limitations in estimating changes in hydrological extremes, especially for compound hydrological events. The large ensemble method does not suffer from these limitations and quantifies the impacts of climate change with greater precision. The explicit simulation of extreme events enables better hydrological process understanding. We conclude that future studies focusing on the impact of climatic changes on hydrological extremes should use large ensemble techniques to properly account for these rare hydrological events.