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.

翻译：准确且高分辨率的地球系统模型（ESM）模拟对于评估人为气候变化对生态和社会经济的影响至关重要，但其计算成本过高。近期机器学习方法在降尺度ESM模拟方面展现出前景，其性能优于最先进的统计方法。然而，现有方法需为每个ESM重新训练，计算成本高昂，且对训练期间未见气候状态的泛化能力较差。我们通过训练一致性模型（CM）解决了上述局限，该模型能够以零样本方式高效且准确地降尺度任意ESM模拟，无需重新训练。我们的基础模型方法可生成仅受观测参考数据分辨率限制的概率性降尺度场。研究表明，该一致性模型以极低计算成本优于最先进的扩散模型，同时保持对降尺度任务的高度可控性。此外，该方法无需显式物理约束即可泛化至训练期间未见的气候状态。