DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts Using Diffusion Models

Renewable energy generation is highly sensitive to both local and large‐scale atmospheric dynamics. Skillful subseasonal to seasonal (S2S) forecasts—ranging from 2 weeks to two months—can provide substantial socioeconomic benefits for the energy sector. In this work, we introduce DiffScale , a diffusion‐based generative model that super‐resolves surface wind speed forecasts by flexibly downscaling coarse‐resolution S2S predictions. DiffScale leverages conditional guidance from low‐resolution weather forecasts to sample high‐resolution realizations, enabling the direct estimation of the conditional density over target wind fields without relying on auto‐regressive or sequence‐based methods. This formulation yields a computationally efficient and scalable framework for continuous downscaling across arbitrary spatial resolutions and forecast lead times. We evaluate DiffScale on synthetic experiments that involve downscaling ECMWF S2S forecasts to the higher‐resolution ERA5 reanalysis data. The key innovation lies in its generalization ability: DiffScale adapts to varying downscaling factors and temporal horizons without retraining, while effectively correcting biases in the original forecasts. Empirical results demonstrate that DiffScale consistently improves both deterministic and probabilistic forecast skill over ECMWF baselines and their quantile‐mapped variants. Moreover, it achieves competitive performance compared to climatology up to lead time week 6, underscoring its potential for enhancing real‐time wind speed forecasts in energy applications.