Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis (PP) approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalise is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use-case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on eXplainable artifical intelligence (XAI) techniques. We show how these techniques can be used to unravel the internal behaviour of these models, providing new evaluation dimensions and aiding in their diagnostic and design. These results show the usefulness of incorporating XAI techniques into statistical downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions.

翻译：深度学习（DL）已成为一种有前景的工具，用于按照完美预报（PP）方法从大尺度大气场将气候预估降尺度到区域至局地尺度。鉴于其复杂性，对这些方法进行恰当评估至关重要，尤其是在应用于气候变化条件时，其外推/泛化能力是关键。在本研究中，我们针对同一具有挑战性的用例（CORDEX北美区域的气温降尺度），对文献中提取的几种深度学习模型进行了相互比较，并基于可解释人工智能（XAI）技术扩展了标准评估方法。我们展示了这些技术如何用于揭示这些模型的内部行为，提供新的评估维度，并助力其诊断与设计。这些结果表明，将XAI技术纳入统计降尺度评估框架具有实用性，尤其是在处理大区域和/或气候变化条件下。