Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state-of-the-art method to produce localized future climate information, involves running a regional climate model (RCM) driven by an Earth System Model (ESM), but it is too computationally expensive to apply to large climate projection ensembles. We propose a novel approach combining dynamical downscaling with generative artificial intelligence to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale. This approach leverages the generalizability of physics-based models and the sampling efficiency of diffusion models, enabling the downscaling of large multi-model ensembles. We evaluate our method against dynamically-downscaled climate projections from the CMIP6 ensemble. Our results demonstrate its ability to provide more accurate uncertainty bounds on future regional climate than alternatives such as dynamical downscaling of smaller ensembles, or traditional empirical statistical downscaling methods. We also show that dynamical-generative downscaling results in significantly lower errors than bias correction and spatial disaggregation (BCSD), and captures more accurately the spectra and multivariate correlations of meteorological fields. These characteristics make the dynamical-generative framework a flexible, accurate, and efficient way to downscale large ensembles of climate projections, currently out of reach for pure dynamical downscaling.

翻译：区域高分辨率气候预测对农业、水文及自然灾害风险评估等诸多应用至关重要。动态降尺度作为生成局地化未来气候信息的先进方法，需运行由地球系统模型驱动的区域气候模型，但其计算成本过高，难以应用于大规模气候预测集合。本文提出一种融合动态降尺度与生成式人工智能的新方法，以降低降尺度气候预测的成本并改进其不确定性估计。在该框架中，区域气候模型先将地球系统模型输出动态降尺度至中间分辨率，再由生成式扩散模型进一步细化至目标分辨率。该方法结合了基于物理模型的普适性与扩散模型的采样效率，实现了对大型多模型集合的降尺度处理。我们使用CMIP6集合的动态降尺度气候预测数据对本方法进行评估。结果表明：相较于小规模集合的动态降尺度或传统经验统计降尺度方法，本方法能为未来区域气候提供更准确的不确定性边界。研究同时证明，动态-生成式降尺度相比偏差校正与空间解聚法具有显著更低的误差，并能更精确地捕捉气象场的频谱特征与多元相关性。这些特性使动态-生成式框架成为一种灵活、准确且高效的大规模气候预测集合降尺度方法，而目前纯动态降尺度技术尚无法实现此类大规模应用。