DriveGuard-WM: Dynamic-Only Cross-Region Meteorological Downscaling

Supervised meteorological downscaling relies on paired low-resolution (LR) and high-resolution (HR) data, yet observationally supported HR data remain geographically limited. This motivates cross-region downscaling from paired source-region data to target regions with only LR inputs. However, regional differences in spatiotemporal dynamics limit the transferability of learned reconstruction relationships. Our central premise is that transfer should account for the predictive contributions of source-learned relationships and adapt their use to current LR conditions. To address this challenge, we propose DriveGuard-WM, a cross-region downscaling framework that reconstructs HR fields from dynamic LR inputs alone, without target HR supervision or fine-tuning. The framework translates contribution assessments from a world model into LR-conditioned guidance for adaptive spatiotemporal fusion. Across six directed transfers among Australia, the contiguous United States, and Europe, DriveGuard-WM reduces macro-averaged, source-standardized mean absolute error (MAE) by 38.6% for temperature and 46.0% for precipitation relative to the best evaluated baseline on this metric under the reported reference-field protocol. Our code is available at https://anonymous.4open.science/r/WeatherDownscaling-7732/.