Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input.

翻译：多模型集成分析将来自多个气候模型的信息融合成一个统一的预测，但是现有的集成方法基于模型平均可能会稀释精细的空间信息，并从重新缩放低分辨率气候模型中引入偏差。我们提出了一种统计方法，称为NN-GPR，它使用了高斯过程回归（GPR）和基于无限宽度深度神经网络的协方差函数。NN-GPR不需要关于模型之间关系的任何假设，也不需要插值到共同网格，不需要任何平稳性假设，并且在其预测算法的一部分中自动进行下采样。模型实验表明，NN-GPR可以通过保留多个尺度的地理空间信号并捕获年际变化来预测表面温度和降水，其预测性能非常高。我们的预测特别展现了在高变异性的区域中提高的准确性和不确定性量化技能，这使我们可以在0.44$^\circ$/50 km的空间分辨率下廉价地评估尾部行为，而无需使用区域气候模型（RCM）。在再分析数据和SSP245气候强迫模型上的评估表明，NN-GPR的整体气候与模型集合相似，同时更好地捕获了精细的空间图案。最后，我们将NN-GPR的区域预测与两个RCM的预测进行比较，并表明NN-GPR可以只使用全球模型数据作为输入而与RCM的性能媲美。