Hyper-local, efficient extreme heat projection and analysis using machine learning to augment a hybrid dynamical-statistical downscaling technique

This paper describes a scalable system for quantifying hyper-local heat stress in urban environments and its expected response within the changing climate. A hybrid dynamical-statistical downscaling approach links Global Climate Models (GCMs) with dynamically downscaled extreme heat events using the Weather Research and Forecasting model (WRF). Downscaled historical simulations in WRF incorporate urban canopy physics to better describe localized...