Trevor Harris, Bo Li, Ryan Sriver

Research output: Contribution to journalArticlepeer-review


Multimodel ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration ap-proaches, 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 climate models, no interpolation to a common grid, 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 pre-serving geospatial signals at multiple scales and capturing interannual vari-ability. 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 /50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP2-4.5 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.

Original languageEnglish (US)
Pages (from-to)3403-3425
Number of pages23
JournalAnnals of Applied Statistics
Issue number4
StatePublished - Dec 2023


  • Gaussian process regression
  • Multimodel ensembles
  • climate model integration
  • deep learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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