ARGO: A Regional-Global Joint Method for Region-Flexible Weather Forecasting

Regional weather forecasting is crucial for many domains such as transportation, energy and disaster prevention. Existing data-driven methods typically perform global forecasting directly or build weather forecasting systems tailored to specific regions. However these paradigms present two key limitations: (1) generalization across regions with different geographical locations and spatial extents, and (2) filtering relevant global information to guide regional prediction. To address these challenges, we propose ARGO, a regional–global joint method for region-flexible weather forecasting. To support regions with varying locations and spatial extents, ARGO combines Geo-aware Position Encoding (GaPE), random regional sampling, and an auxiliary alignment loss to learn transferable forecasting patterns and compatible geographical representations. To filter global information, a Global Token Router (GTR) selects relevant nearby and remote tokens while retaining broad global context, followed by latent-space cross-attention for regional–global fusion. Experiments at 0.25${}^{\circ }$ resolution show that ARGO outperforms state-of-the-art baselines in regional forecasting and generalizes across different regions without retraining from scratch.