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This paper introduces a universal diffusion-based downscaling framework that converts low-resolution weather forecasts into high-resolution probabilistic predictions without model-specific fine-tuning. A conditional diffusion model is trained on coarse-resolution inputs and high-resolution reanalysis targets and then applied in a zero-shot manner to deterministic forecasts from various weather models. The downscaled forecasts consistently improve upon the raw deterministic forecasts, with significant gains in probabilistic skill (CRPS) when evaluated against independent station observations.
A single diffusion model can probabilistically downscale weather forecasts from diverse models in a zero-shot manner, boosting forecast accuracy and uncertainty representation without model-specific fine-tuning.
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.