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While widely recognized as one of the most substantial weather forecastingmethodologies, Numerical Weather Prediction (NWP) usually suffers fromrelatively coarse resolution and inevitable bias due to tempo-spatialdiscretization, physical parametrization process, and computation limitation.With the roaring growth of deep learning-based techniques, we propose theDual-Stage Adaptive Framework (DSAF), a novel framework to address regional NWPdownscaling and bias correction tasks. DSAF uniquely incorporates adaptiveelements in its design to ensure a flexible response to evolving weatherconditions. Specifically, NWP downscaling and correction are well-decoupled inthe framework and can be applied independently, which strategically guides theoptimization trajectory of the model. Utilizing a multi-task learning mechanismand an uncertainty-weighted loss function, DSAF facilitates balanced trainingacross various weather factors. Additionally, our specifically designedattention-centric learnable module effectively integrates geographicinformation, proficiently managing complex interrelationships. Experimentalvalidation on the ECMWF operational forecast (HRES) and reanalysis (ERA5)archive demonstrates DSAF's superior performance over existing state-of-the-artmodels and shows substantial improvements when existing models are augmentedusing our proposed modules. Code is publicly available athttps://github.com/pengwei07/DSAF.

DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling