From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model

Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics. Traditional limited-area models rely on computationally expensive simulations, while many learning-based approaches frame the problem as super-resolution, overlooking statistical and physical mismatches across scales. We propose a foundation-model-driven downscaling framework that learns regional refinements of global forecasts by augmenting a pretrained weather model backbone with lightweight, multi-scale prediction heads operating directly in its latent space. Despite being pretrained on substantially coarser data, the backbone supports regional adaptation at a tenfold finer grid spacing, without retraining it. The proposed approach uses operational global analyses as inputs and high-resolution regional NWP fields as training targets. It is evaluated not only against gridded datasets but also against ground-based weather station observations, enabling analysis of systematic biases between global analyses, regional WRF forecasts, and real world mesurements. Our model outperforms or matches operational WRF-ARW forecasts on all surface variables and achieves lower RMSE on upper-air variables, at a fraction of the computational cost. It also shows markedly better rollout stability.