Potential Predictability of Regional Precipitation and Discharge Extremes using Synoptic-Scale Climate Information via Machine Learning: An Evaluation for the Eastern Continental United States

AbstractCurrent generation General Circulation Models (GCMs) simulate synoptic-scale climate state variables such geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than meso-scale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state informed downscaling impose some limitations on the skill of precipitation reproduction, including: hard clustering of climate modes into a discrete set of states; utilization of numerical clustering methodologies poorly suited to non-normal data; and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic scale climate state variables. Archetypal Ana...