  We describe an improved statistical downscaling method for Earth science
applications using multivariate Basis Graphical Lasso (BGL). We demonstrate our
method using a case study of sea surface temperature (SST) projections from
CMIP6 Earth system models, which has direct applications for studies of
multi-decadal projections of coral reef bleaching. We find that the BGL
downscaling method is computationally tractable for large data sets, and that
mean squared predictive error is roughly 8% lower than the current
state-of-the-art interpolation-based statistical downscaling method. Finally,
unlike most ofthe currently available methods, BGL downscaling produces
uncertainty estimates. Our novel method can be applied to any model output
variable for which corresponding higher-resolution observational data is
available.
