Abstract The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high‐resolution weather information. Coarse‐grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large‐scale forcing. We address these challenges by learning the climatological prior distribution of a target region with a generative model, using its high‐resolution numerical weather simulations. An optimal combination of this learned high‐resolution climatological prior with coarse‐grid large scale forecasts yields highly accurate, fine‐grained, full‐variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting‐downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100‐member, 10‐day forecast with spatial resolution of 1 km and output frequency of 15 min takes 1 hr on a moderate‐end GPU, as contrast to CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, this paradigm paves the way for more efficient and reliable renewable energy planning and operation.
Wang et al. (2026) studied this question.