We introduce a universal diffusion-based downscaling framework that lifts deterministic low-resolution weather forecasts into probabilistic high-resolution predictions without any model-specific fine-tuning. A single conditional diffusion model is trained on paired coarse-resolution inputs (~25 km resolution) and high-resolution regional reanalysis targets (~5 km resolution), and is applied in a fully zero-shot manner to deterministic forecasts from heterogeneous upstream weather models. Focusing on near-surface variables, we evaluate probabilistic forecasts against independent in situ station observations over lead times up to 90 h. Across a diverse set of AI-based and numerical weather prediction (NWP) systems, the ensemble mean of the downscaled forecasts consistently improves upon each model's own raw deterministic forecast, and substantially larger gains are observed in probabilistic skill as measured by CRPS. These results demonstrate that diffusion-based downscaling provides a scalable, model-agnostic probabilistic interface for enhancing spatial resolution and uncertainty representation in operational weather forecasting pipelines.

翻译：本文提出了一种通用的基于扩散模型的降尺度框架，该框架能够将确定性低分辨率天气预报提升为概率性高分辨率预测，且无需任何模型特定的微调。我们基于配对粗分辨率输入（约25公里分辨率）和高分辨率区域再分析目标（约5公里分辨率）训练单一条件扩散模型，并以完全零样本方式将其应用于异构上游天气模型的确定性预报。聚焦于近地表变量，我们针对长达90小时的预报时效，利用独立地面站点观测数据评估概率预报性能。在涵盖多种基于人工智能的数值天气预报系统范围内，降尺度预报的集合平均持续优于各模型自身的原始确定性预报，且以连续分级概率评分衡量的概率技巧提升更为显著。这些结果表明，基于扩散模型的降尺度方法为业务天气预报流程提供了一种可扩展、模型无关的概率接口，有效增强了空间分辨率与不确定性表征能力。