Assessment of a Pan-European high-resolution downscaling through Deep Learning

The performance of a deep convolutional neural network in predicting near-surface air temperature (T2m) and total precipitation (P) over Europe is assessed, comparing its results with the Copernicus European Regional Reanalysis (CERRA) and the regional dynamical model HCLIM. The ML-model accurately captures broad seasonal temperature and precipitation patterns, with minor biases in summer and more pronounced warm biases in winter. While the model effectively reproduces the probability density functions (PDFs) of daily temperature and precipitation, it underestimates extreme cold events and the high precipitation extremes in some regions. Climate indices, including cold extremes (TM2PCTL), warm extremes (TM98PCTL), consecutive dry days (CDD), and consecutive wet days (CWD), highlight that the ML model aligns closely with CERRA. However, it slightly underestimates CDD and overestimates CWD, particularly in mountainous and Mediterranean regions. Analyses of spatio-temporal variability demonstrate high correlations with CERRA for temperature, exceeding 0.99 for spatial patterns and 0.95 for temporal correlations, while correlations for precipitation are lower, with underestimated temporal variability. The ML model generally outperforms HCLIM, particularly in aligning with observed data, although challenges remain in capturing extremes and reducing biases in certain regions. These results further highlight the potential of the ML model for regional climate downscaling and impact studies, while emphasizing the need for further refinement to enhance its representation of extreme events and improve spatial accuracy.