Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.

翻译：气候降尺度是指从低分辨率模拟数据生成高分辨率气候数据的过程，这对于在区域和地方尺度上理解和适应气候变化至关重要。深度学习方法已被证明在解决此问题上具有效用。然而，现有研究通常专注于针对特定任务、地点和变量训练模型，因此其普适性和可迁移性有限。在本文中，我们评估了在多个多样化气候数据集上训练深度学习降尺度模型以学习更稳健和可迁移表征的效能。我们使用卷积神经网络（CNNs）、傅里叶神经算子（FNOs）和视觉Transformer（ViTs）评估了架构的零样本可迁移性。我们通过实验评估了降尺度模型在空间、变量和产品上的可迁移性，以理解这些不同架构类型的泛化能力。