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.
@article{arxiv.2602.11893,
title = {Universal Diffusion-Based Probabilistic Downscaling},
author = {Roberto Molinaro and Niall Siegenheim and Henry Martin and Mark Frey and Niels Poulsen and Philipp Seitz and Marvin Vincent Gabler},
journal= {arXiv preprint arXiv:2602.11893},
year = {2026}
}
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ICLR 2026 Workshop on AI and Partial Differential Equations