Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data, capturing fine-scale spatial patterns and variability. Downscaling is any method to derive high-resolution data from low-resolution variables, often to provide more detailed and local predictions and analyses. This problem is societally crucial for effective adaptation, mitigation, and resilience against significant risks from climate change. The challenge arises from spatial heterogeneity and the need to recover finer-scale features while ensuring model generalization. Most downscaling methods \cite{Li2020} fail to capture the spatial dependencies at finer scales and underperform on real-world climate datasets, such as sea-level rise. We propose a novel Kriging-informed Conditional Diffusion Probabilistic Model (Ki-CDPM) to capture spatial variability while preserving fine-scale features. Experimental results on climate data show that our proposed method is more accurate than state-of-the-art downscaling techniques.

翻译：给定来自全球气候模型或卫星数据的较低分辨率预测，降尺度问题旨在估算更高分辨率的区域气候数据，以捕捉精细尺度的空间格局与变异性。降尺度泛指任何从低分辨率变量推导高分辨率数据的方法，通常旨在提供更详细、更局部的预测与分析。该问题对于有效适应、减缓气候变化及抵御其带来的重大风险具有至关重要的社会意义。其挑战源于空间异质性，以及需要在确保模型泛化能力的同时恢复精细尺度特征。大多数降尺度方法 \cite{Li2020} 未能捕捉精细尺度的空间依赖性，且在真实世界气候数据集（如海平面上升数据）上表现欠佳。我们提出了一种新颖的克里金信息条件扩散概率模型（Ki-CDPM），以在保持精细尺度特征的同时捕捉空间变异性。在气候数据上的实验结果表明，我们提出的方法比现有最先进的降尺度技术更为准确。