Europe’s extreme temperatures and rainfall in finer detail: strengths and limits of climate model downscaling
Climate extremes in Europe are becoming increasingly frequent and severe, heightening the need for reliable regional climate information to support preparedness and adaptation. Downscaled climate model (DCM) products are widely used for this purpose. Yet, their accuracy relative to global climate models (GCMs) and observations remains uncertain, particularly for extremes. We evaluated DCM ensembles (EURO-CORDEX, NEX-GDDP-CMIP6, GDPCIR) against GCMs (CMIP5, CMIP6) and reanalysis datasets (ERA5, GMFD), using gridded observational data (E-OBS) as reference. Model accuracy was assessed for mean and extreme temperature and precipitation across Europe using complementary metrics that quantify distributional accuracy, pointwise agreement, and bias magnitude and direction. The results show that downscaling can add details to and increase the accuracy of modeled climate variables in Europe. Statistically downscaled and bias-corrected products NEX-GDDP-CMIP6 and GDPCIR generally outperform their driving GCM for mean and extreme temperature, and for mean precipitation, whereas gains for extreme precipitation are limited. Compared to E-OBS, the dynamically downscaled ensemble EURO-CORDEX exhibits some strong regional biases, notably for temperature. No single dataset performs best in all regions. Across datasets, accuracy is reduced in areas with complex terrain (mountains and coastlines). Uncertainties of the observational benchmark further complicate the evaluation. Our findings highlight both the value and limitations of climate model downscaling: while DCMs provide critical fine-scale information, their reliability is variable- and region-dependent, with extreme precipitation and conditions in complex terrain remaining particularly difficult to capture.