SR-Weather: Super-Resolution Machine Learning Weather Forecast for 1-km Air Temperature Prediction

Machine learning-based global weather forecasts often suffer from coarse spatial resolution, limiting their ability to capture fine-scale temperature variability in regions with complex terrain or strong urban–rural gradients. We present SR-Weather, a two-stage deep learning framework that downscales coarse 0.25° forecasts into 1 km air temperature fields. Our model is trained using ERA5 and MODIS-derived temperature data, and leverages high-resolution auxiliary inputs, including elevation, impervious surface fraction, and spatial information–normalized air temperature to enhance spatial fidelity. Applied to 7-day lead forecasts from the FuXi model, SR-Weather consistently outperforms FuXi’s own 1-day lead predictions, indicating strong capabilities in both resolution enhancement and bias correction. The model also exhibits robustness under cloud-contaminated MODIS observations by reconstructing missing temperature values using auxiliary data. While developed and validated over South Korea, SR-Weather is region-agnostic and applicable globally due to the availability of MODIS inputs and minimal reliance on localized data. These results position SR-Weather as a scalable solution for high-resolution, ML-based weather forecasting.