Chunguang Yin

Abstract Mesoscale convective systems generate heavy-tailed extreme rainfall that conventional numerical weather prediction and standard deep learning architectures consistently underestimate, creating persistent forecasting bottlenecks over coastal urban zones. This study develops ConvScaleCast, a physics-informed scale-bridging framework generating 0–24 h precipitation predictions through integrated multi-scale atmospheric information fusion.