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 multiple DCM ensembles against GCMs and reanalysis datasets, using gridded observational data as reference. Model accuracy was assessed for mean and extreme temperature and precipitation across Europe using complementary metrics that quantify distributional agreement, pointwise accuracy, and bias magnitude and direction. The results show that downscaling generally improves the representation of European climate. Statistically downscaled and bias-corrected products outperform GCMs for mean and extreme temperature, and for mean precipitation, whereas gains for extreme precipitation are limited. The dynamically downscaled ensemble exhibits systematic regional biases, notably for temperature in the Nordics. No single dataset performs best across all regions. Across models, accuracy is reduced in areas with complex terrain (mountains and coastlines). Observational uncertainty further complicates bias assessment. 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 remaining particularly difficult to capture.