Copula based covariate selection in climate for statistical downscaling

It is imperative to accurately assess the impacts of climate change at regional scale in order to inform stakeholders to make policy decisions on critical infrastructures, management of natural resources, humanitarian aid, and emergency preparedness. However, Global Climate Models (GCMs) currently provide relatively coarse resolution outputs which preclude their application to accurately assess the effects of climate change on finer regional scale events. Statistical downscaling are methods that use statistical models to infer the regional-scale or local-scale climate information from coarsely resolved climate models. To make accurate predictions, covariate selection must be used to reduce the dimensionality of high dimensional climate data. Covariates in climate data tend to be highly dependent and non-linear in nature requiring advanced covariate selection methods. In this work, we propose a novel copula-based dependence measure that can capture non-linear relationships between variables as a criterion for feature selection. We demonstrate its effectiveness in discovering relevant features important for prediction with a non-parametric Bayesian mixture of sparse regression models applied to statistical downscaling.