Climate Model Downscaling: Spatial Models and Uncertainty Quantification

We present a statistical downscaling framework for generating fine-resolution climate projections by integrating high-resolution remote sensing data with coarse-resolution outputs from Earth system models. The proposed multivariate spatial statistical model leverages a basis function representation to enable scalable computation while accommodating potentially nonstationary spatial dependence. We apply the method to downscale sea surface temperature (SST) projections over the Great Barrier Reef region using CMIP6 model outputs. Results demonstrate substantial reductions in mean squared predictive error relative to state-of-the-art approaches, while also providing full predictive distributions that support comprehensive uncertainty quantification.