Evaluation of Projected Changes in European Rx1day Before and After Bias Correction

Climate change projections of extreme precipitation are commonly derived from large multi-model ensembles such as EURO-CORDEX, where individual model combinations show systematic biases relative to observations. It is not clear how bias correction affects both model agreement and the projected change signal itself, or whether raw model performance provides a reliable basis for ranking ensemble members. Using annual maximum 1-day precipitation (Rx1day) as a case study, we evaluate a EURO-CORDEX ensemble against the E-OBS and ERA5 observational datasets, both in raw form and after bias correction with Empirical Quantile Mapping (EQM), mean correction mapping, and Quantile Delta Mapping (QDM). We find that historical model performance is not predictive of the magnitude of projected change and that bias correction reduces inter-model spread in the historical period, bringing most models to a level of agreement with observations comparable to the disagreement between the observational datasets themselves. Further, systematic differences in the projected change signal between certain GCMs are clear, particularly in the tails of the distribution and in regions such as the Alps and the Adriatic Sea, showing that differences in model skill remain both prior to and after bias correction. Finally, the choice of correction method matters: EQM distorts the projected change signal more than the trend-preserving QDM. Taken together, bias correction should be used as a tool for understanding how models perform, but it should not be relied on as an absolute truth.