  While widely recognized as one of the most substantial weather forecasting
methodologies, Numerical Weather Prediction (NWP) usually suffers from
relatively coarse resolution and inevitable bias due to tempo-spatial
discretization, physical parametrization process, and computation limitation.
With the roaring growth of deep learning-based techniques, we propose the
Dual-Stage Adaptive Framework (DSAF), a novel framework to address regional NWP
downscaling and bias correction tasks. DSAF uniquely incorporates adaptive
elements in its design to ensure a flexible response to evolving weather
conditions. Specifically, NWP downscaling and correction are well-decoupled in
the framework and can be applied independently, which strategically guides the
optimization trajectory of the model. Utilizing a multi-task learning mechanism
and an uncertainty-weighted loss function, DSAF facilitates balanced training
across various weather factors. Additionally, our specifically designed
attention-centric learnable module effectively integrates geographic
information, proficiently managing complex interrelationships. Experimental
validation on the ECMWF operational forecast (HRES) and reanalysis (ERA5)
archive demonstrates DSAF's superior performance over existing state-of-the-art
models and shows substantial improvements when existing models are augmented
using our proposed modules. Code is publicly available at
https://github.com/pengwei07/DSAF.
