Using Machine Learning to Cut the Cost of Dynamical Downscaling

Abstract

Global climate models (GCMs) are commonly downscaled to understand future local climate change. The high computational cost of regional climate models (RCMs) limits how many GCMs can be dynamically downscaled, restricting uncertainty assessment. While statistical downscaling is cheaper, its validity in a changing climate is unclear. We combine these approaches to build an emulator leveraging the merits of dynamical and statistical downscaling. A machine learning model is developed for each coarse grid cell to predict fine grid variables, using coarse-scale climate predictors with fine grid land characteristics. Two RCM emulators, one Multilayer Perceptron (MLP) and one Multiple Linear Regres..