  Accurate and high-resolution Earth system model (ESM) simulations are
essential to assess the ecological and socio-economic impacts of anthropogenic
climate change, but are computationally too expensive. Recent machine learning
approaches have shown promising results in downscaling ESM simulations,
outperforming state-of-the-art statistical approaches. However, existing
methods require computationally costly retraining for each ESM and extrapolate
poorly to climates unseen during training. We address these shortcomings by
learning a consistency model (CM) that efficiently and accurately downscales
arbitrary ESM simulations without retraining in a zero-shot manner. Our
foundation model approach yields probabilistic downscaled fields at resolution
only limited by the observational reference data. We show that the CM
outperforms state-of-the-art diffusion models at a fraction of computational
cost while maintaining high controllability on the downscaling task. Further,
our method generalizes to climate states unseen during training without
explicitly formulated physical constraints.
