Subin Kim

To downscale artificial intelligence (AI) medium-range forecasts over the Korean Peninsula, we developed a 1.5 km CorrDiff system trained with ERA5 inputs and UM-LDAPS analyses as targets. For 1.5 m temperature and 10 m wind, the regression stage reduced root-mean-square error (RMSE) and root-mean-square vector error (RMSVE) relative to ERA5 and increased spatial correlation. CorrDiff had smaller absolute biases and greater spatial variability than Regression but higher RMSE and RMSVE and lower spatial correlation. The most consistent improvement relative to Regression occurred for high-temperature events at or above the target 99th percentile, with higher probability of detection (POD) and critical success index (CSI) and lower conditional temperature error. Without additional training or correction, AI Forecasting System (AIFS) forecasts were downscaled through +288 h. In a July 2026 heatwave case at 47 stations, CorrDiff reduced temperature errors and alleviated the underprediction of high-temperature occurrence relative to Regression. CorrDiff had higher POD and CSI than the AIFS for temperatures ≥33 °C but higher RMSE and lower spatial correlation. Generative downscaling should therefore be evaluated for spatial variability, spatial agreement, and event-specific performance beyond deterministic errors; producing high-resolution data does not necessarily imply improved forecast performance against observations.