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：多模型集合分析将来自多个气候模型的信息整合为统一预测。然而，现有基于模型平均的整合方法会稀释精细尺度空间信息，并因对低分辨率气候模型进行重采样而产生偏差。本文提出一种名为NN-GPR的统计方法，该方法采用基于无限宽深度神经网络协方差函数的高斯过程回归（GPR）。NN-GPR无需假设模型间关系、无需插值到公共网格、无需平稳性假设，并在预测算法中自动实现降尺度。模型实验表明，NN-GPR通过保留多尺度地理空间信号并捕捉年际变率，在地表温度和降水预报方面具有卓越技巧。我们的预测尤其在高变率区域展现出改进的精度和不确定性量化能力，从而能以0.44°/50公里空间分辨率低成本评估尾部行为，无需使用区域气候模型（RCM）。对再分析数据和SSP245强迫气候模型的评估显示，NN-GPR能生成与模型集合总体相似的气候态，同时更优地捕捉精细尺度空间模式。最后，我们将NN-GPR的区域预测与两个RCM进行对比，结果表明仅使用全球模型数据作为输入的NN-GPR能达到与RCM相当的性能。