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 (ML) 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 ML framework emulating 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. To efficiently model the high-dimensional output, the super-resolution step employs a novel class of sparse local stochastic layers. 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 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 and temporal structure, extremes, and multivariate dependencies. Comparison with diverse benchmarks demonstrates EnScale(-t)'s competitive performance and computational efficiency, offering a promising approach for accurate and temporally consistent RCM emulation.

翻译：全球环流模型（GCMs）的未来气候预估在实际应用中常受限于其粗糙的空间分辨率，需通过降尺度生成高分辨率数据。区域气候模型（RCMs）能够实现这种精细化，但计算成本高昂。为解决此问题，机器学习（ML）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域。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，因此难以精确建模。我们提出EnScale，一种通过训练多对GCM与对应RCM数据来模拟完整GCM-to-RCM映射的生成式ML框架。该方法首先调整GCM与粗化RCM数据之间的大尺度偏差，随后通过超分辨率步骤生成高分辨率场域。为高效建模高维输出，超分辨率步骤采用新型稀疏局部随机层。两步均采用基于能量分数（一种恰当评分规则）优化的生成模型。与现有最优ML降尺度方法相比，我们的方案将计算成本降低约一个数量级。EnScale可联合模拟中欧地区空间一致的多变量——温度、降水、太阳辐射与风。此外，我们提出变体EnScale-t以实现时序一致降尺度。我们建立了涵盖校准、空间与时间结构、极端事件及多变量依赖性等多类别的综合评估框架。与多种基准方法的对比表明，EnScale(-t)在性能与计算效率上具有竞争力，为精确且时序一致的RCM模拟提供了有前景的途径。