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Abstract We propose a statistical downscaling method to produce fine-resolution climate projections. A multivariate spatial statistical model is developed to jointly analyse high-resolution remote sensing data and coarse-resolution climate model outputs. With a basis function representation, the resulting model can achieve efficient computation and describe potentially nonstationary spatial dependence. We implement our method to produce downscaled sea surface temperature projections over the Great Barrier Reef region from CMIP6 Earth system models. Compared with the state of the art, our method reduces the mean squared predictive error substantially and produces a predictive distribution enabling holistic uncertainty quantification analyses. 

Abstract Simulations of future climate contain variability arising from a number of sources, including internal stochasticity and external forcings. However, to the best of our abilities climate models and the true observed climate depend on the same underlying physical processes. In this paper, we simultaneously study the outputs of multiple climate simulation models and observed data, and we seek to leverage their mean structure as well as interdependencies that may reflect the climate’s response to shared forcings. Bayesian modeling provides a fruitful ground for the nuanced combination of multiple climate simulations. We introduce one such approach whereby a Gaussian process is used to represent a mean function common to all simulated and observed climates. Dependent random effects encode possible information contained within and between the plurality of climate model outputs and observed climate data. We propose an empirical Bayes approach to analyze such models in a computationally efficient way. This methodology is amenable to the CMIP6 model ensemble, and we demonstrate its efficacy at forecasting global average near-surface air temperature. Results suggest that this model and the extensions it engenders may provide value to climate prediction and uncertainty quantification.