Hangyi Yu

Abstract Near-surface weather variables play an important role in production. However, the coarse spatial resolution meteorological data in existing meteorological products limit their applicability in modern high-precision engineering and decision-making scenarios. To address this limitation, we propose a deep learning–based meteorological downscaling framework, Hybrid Spatial-Field Attention Network (HSFANet).