Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth's climate. One key application of these models is studying extreme weather events, such as heat waves or dry spells, which have significant socioeconomic and environmental consequences. However, the computational demands of running a sufficient number of simulations to analyze the risks are often prohibitive. In this paper we demonstrate that diffusion models -- a class of generative deep learning models -- can effectively emulate the spatio-temporal trends of ESMs under previously unseen climate scenarios, while only requiring a small fraction of the computational resources. We present a diffusion model that is conditioned on monthly averages of temperature or precipitation on a $96 \times 96$ global grid, and produces daily values that are both realistic and consistent with those averages. Our results show that the output from our diffusion model closely matches the spatio-temporal behavior of the ESM it emulates in terms of the frequency of phenomena such as heat waves, dry spells, or rainfall intensity.

翻译：地球系统模型（ESMs）是理解人类活动对地球气候影响的重要工具。这些模型的关键应用之一是研究极端天气事件（如热浪或干旱期），这些事件具有重大的社会经济和环境后果。然而，运行足够数量的模拟以分析风险的计算需求往往令人望而却步。本文证明，扩散模型（一种生成式深度学习模型）能够在仅需少量计算资源的情况下，有效仿真地球系统模型在未见气候情景下的时空趋势。我们提出了一种扩散模型，该模型以$96\times 96$全球网格上的月平均温度或降水量为条件，生成既真实又与该平均值一致的日值数据。结果表明，我们扩散模型的输出在热浪、干旱期或降雨强度等现象的频率方面，与其仿真的地球系统模型的时空行为高度吻合。