Enhancing 1-month temperature predictions in South Korea through dynamical downscaling of machine learning global ensemble forecasts

Despite the growing availability of global ensemble forecasts, their application at regional scales remains challenging due to the coarse spatial resolution. Meanwhile, machine learning (ML)-based forecasts are emerging as promising alternatives to traditional physics-based models, yet they are mostly deterministic, and their potential has not been fully explored beyond the medium-range timeframe. This study investigates the potential of integrating global ML-based ensemble forecasts (FuXi-ENS) with dynamical downscaling to improve one-month temperature prediction over South Korea. The forecasting performance of FuXi-ENS, in terms of both temporal and spatial patterns, is compared against state-of-the-art physics-based model forecasting data from NOAA and ECMWF, which serve as benchmarks. The superiority of FuXi-ENS becomes pronounced after dynamical downscaling, highlighting the added value of ML-based forecasts when combined with high-resolution physical modeling. Overall, this study offers a comprehensive assessment of extended-range temperature prediction over South Korea, illustrating the operational potential of hybrid approaches that combine global ML models with regional dynamical downscaling and providing insights for the future development of hybrid forecasting systems.