Addressing the challenges of climate change requires accurate and high-resolution mapping of geospatial data, especially climate and weather variables. However, many existing geospatial datasets, such as the gridded outputs of the state-of-the-art numerical climate models (e.g., general circulation models), are only available at very coarse spatial resolutions due to the model complexity and extremely high computational demand. Deep-learning-based methods, particularly generative adversarial networks (GANs) and their variants, have proved effective for refining natural images and have shown great promise in improving geospatial datasets. This paper describes a conditional GAN-based stochastic geospatial downscaling method that can accommodates very high scaling factors. Compared to most existing methods, the method can generate high-resolution accurate climate datasets from very low-resolution inputs. More importantly, the method explicitly considers the uncertainty inherent to the downscaling process that tends to be ignored in existing methods. Given an input, the method can produce a multitude of plausible high-resolution samples instead of one single deterministic result. These samples allow for an empirical exploration and inferences of model uncertainty and robustness. With a case study of gridded climate datasets (wind velocity and solar irradiance), we demonstrate the performances of the framework in downscaling tasks with large scaling factors (up to $64\times$) and highlight the advantages of the framework with a comprehensive comparison with commonly used and most recent downscaling methods, including area-to-point (ATP) kriging, deep image prior (DIP), enhanced super-resolution generative adversarial networks (ESRGAN), physics-informed resolution-enhancing GAN (PhIRE GAN), and an efficient diffusion model for remote sensing image super-resolution (EDiffSR).

翻译：应对气候变化挑战需要精确且高分辨率的地理空间数据映射，特别是气候与气象变量。然而，由于模型复杂度与极高的计算需求，许多现有地理空间数据集（例如最先进的数值气候模型——如大气环流模型——的网格化输出）仅能在极粗的空间分辨率下获取。基于深度学习的方法，尤其是生成对抗网络及其变体，已被证明在自然图像精细化方面效果显著，并展现出改进地理空间数据集的巨大潜力。本文提出一种基于条件生成对抗网络的随机地理空间降尺度方法，能够适应极高的缩放因子。与现有大多数方法相比，该方法能从极低分辨率输入生成高分辨率精确气候数据集。更重要的是，该方法显式考虑了降尺度过程中固有的不确定性——这一因素在现有方法中常被忽略。给定输入数据，该方法能生成大量可能的高分辨率样本，而非单一确定性结果。这些样本支持对模型不确定性与稳健性进行实证探索与推断。通过网格化气候数据集（风速与太阳辐照度）的案例研究，我们展示了该框架在大缩放因子（最高达$64\times$）降尺度任务中的性能，并通过与常用及最新降尺度方法——包括区域至点克里金法、深度图像先验、增强型超分辨率生成对抗网络、物理信息分辨率增强生成对抗网络以及遥感图像超分辨率高效扩散模型——的全面对比，凸显了该框架的优势。