The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-resolution data. Regional climate models (RCMs) provide this refinement, but are computationally expensive. To address this issue, machine learning models can learn the downscaling function, mapping coarse GCM outputs to high-resolution fields. Among these, generative approaches aim to capture the full conditional distribution of RCM data given coarse-scale GCM data, which is characterized by large variability and thus challenging to model accurately. We introduce EnScale, a generative machine learning framework that emulates the full GCM-to-RCM map by training on multiple pairs of GCM and corresponding RCM data. It first adjusts large-scale mismatches between GCM and coarsened RCM data, followed by a super-resolution step to generate high-resolution fields. Both steps employ generative models optimized with the energy score, a proper scoring rule. Compared to state-of-the-art ML downscaling approaches, our setup reduces computational cost by about one order of magnitude. EnScale jointly emulates multiple variables -- temperature, precipitation, solar radiation, and wind -- spatially consistent over an area in Central Europe. In addition, we propose a variant EnScale-t that enables temporally consistent downscaling. We establish a comprehensive evaluation framework across various categories including calibration, spatial structure, extremes, and multivariate dependencies. Comparison with diverse benchmarks demonstrates EnScale's strong performance and computational efficiency. EnScale offers a promising approach for accurate and temporally consistent RCM emulation.

翻译：全球环流模型（GCMs）提供的未来气候预测因其空间分辨率较低，在实际应用中常需通过降尺度处理生成高分辨率数据。区域气候模型（RCMs）虽能实现精细化模拟，但计算成本高昂。为解决此问题，机器学习模型可学习降尺度函数，将粗分辨率GCM输出映射至高分辨率场。其中，生成式方法旨在捕捉给定粗尺度GCM数据条件下RCM数据的完整条件分布，该分布具有高度变异性，因此精确建模颇具挑战。本文提出EnScale——一种生成式机器学习框架，通过训练多对GCM与对应RCM数据来模拟完整的GCM到RCM映射过程。该框架首先校正GCM与粗化RCM数据间的大尺度偏差，随后通过超分辨率步骤生成高分辨率场。两个步骤均采用以能量评分（一种严格评分规则）优化的生成模型。相较于前沿的机器学习降尺度方法，本方案将计算成本降低约一个数量级。EnScale能联合模拟中欧区域空间一致的多个变量（温度、降水、太阳辐射及风场）。此外，我们提出变体模型EnScale-t，可实现时间一致的降尺度。我们建立了涵盖校准、空间结构、极端事件及多变量依赖性等多类别的综合评估框架。与多种基准方法的比较表明，EnScale在计算效率与性能方面均表现优异，为精准且时间一致的RCM模拟提供了可行路径。