AI‐Based Regional Emulation for Kilometer‐Scale Dynamical Downscaling

Abstract An AI‐based Limited‐Area Model (LAM) is developed for dynamical downscaling over the Southern Great Plains and the southeastern United States, with strong generalization abilities under diverse boundary conditions. The model is trained using 0.25, 3‐hourly ERA5 as forcings and CONUS404 as targets in 1980–2019, producing 4‐km, hourly dynamical downscaling outputs; it is also connected to a post‐processing model to derive additional diagnostic variables. The model is evaluated across multiple forcing data sets, time periods, and climate regimes. For present‐day downscaling in water years 2021–2024, the model produces stable multiyear simulations with no unrealistic drift; its deterministic verification scores are comparable to other weather‐forecasting‐oriented AI models. The model also generalizes robustly to a 1.0, 6‐hourly non‐ERA5 forcing data set, yielding only minor performance changes. Frontal cyclone and hurricane case studies further demonstrate that the model reconstructs realistic, interpretable weather‐scale dynamical and thermodynamic structure from coarse boundary information. The AI‐based LAM is further tested by downscaling 30‐year global climate model runs in 1980–2010 and 2070–2100, and climate model ensembles in 2025–2027. In this application, the model remains stable at hourly downscaling frequencies for all 30 years and effectively captures climate‐change signals, indicating meaningful generalization across different climate regimes. When downscaling ensembles, the model produces well‐posed member distributions without collapsing the ensemble spread. Overall, the AI‐based LAM of this study offers good downscaling performance and generalization abilities. It provides a practical example of adapting AI weather prediction models for regional climate applications, with generalization across boundary forcing data sets and climate regimes.