Rapid Joint Downscaling of Multiple Atmospheric Fields to Kilometer Scale With Deep Learning

Abstract Predicting weather and climate hazards typically relies on computationally expensive kilometer‐scale numerical models. This study introduces a U‐Net‐based deep learning framework, the Joint Atmospheric fields Downscaling Network (JADNet), for rapid, joint downscaling of multiple atmospheric variables to kilometer resolution. JADNet is trained to map interpolated 25‐km ERA5 (IERA5) reanalysis fields to 4‐km surface fields dynamically downscaled from the latter using the Weather Research and Forecasting model. The proposed model is evaluated against multiple extreme events over the Arabian Peninsula in 2014, including heatwaves, extreme precipitation, extreme surface winds, and a localized Tokar Gap Jet. These case studies indicate that JADNet consistently outperforms IERA5 in reproducing fine‐scale intensity, timing, and spatial patterns. Our results demonstrate that JADNet is a computationally efficient and robust surrogate for traditional dynamical downscaling, offering a powerful tool for scalable high‐resolution weather and climate applications.