Pan‐European High‐Resolution Downscaling Using Deep Learning

Abstract This study assesses the performance of a deep convolutional neural network in predicting near‐surface air temperature (T2m) and total precipitation (P) over Europe, comparing its results with the Copernicus European Regional Reanalysis (CERRA) and the dynamical regional model dynamical regional climate model (HCLIM) simulations. The ML‐model accurately captures broad seasonal temperature and precipitation patterns with minor biases in summer and more pronounced warm biases in winter. Although the model effectively reproduces the probability density functions (PDFs) of daily temperature and precipitation, it underestimates extreme cold events and in some regions also the high precipitation extremes. 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, though it slightly underestimates CDD and overestimates CWD, particularly in mountainous and Mediterranean regions. Analysis of spatiotemporal variability demonstrates high correlations with CERRA for temperature exceeding 0.99 for spatial correlations and 0.95 for temporal correlations, whereas 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.