MIRAGE: Multimodal Radar-Conditioned Graph Learning for Rainfall Interpolation and Forecasting

Precipitation interpolation and forecasting over sparse weather station networks are challenging because rainfall exhibits highly localized and non-stationary dynamics, while ground observations are spatially limited. We propose MIRAGE, a radar-conditioned dynamic graph framework that integrates dense radar fields with sparse station observations for precipitation estimation at locations without local precipitation histories. MIRAGE constructs station-specific multimodal representations through cross-attention and uses them to adapt graph computation in two complementary ways: Dynamic Graph Construction determines where information should propagate, while a Meteorological Hypernetwork determines how propagated information should be transformed. Experiments on KMA and MeteoNet datasets show that MIRAGE achieves strong performance in both spatial interpolation and multi-step forecasting, with particularly consistent improvements at unobserved stations. MIRAGE also maintains competitive performance as observation sparsity increases. Ablation studies further demonstrate the complementary contributions of radar conditioning, cross-modal attention, and adaptive graph computation.