PNNL

Abstract

High-resolution precipitation data, generated through dynamical (DD) or statistical (SD) downscaling of global climate model output, provides critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate grid-scale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g. hydrologic modeling and analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products are desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous U.S. (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and non-parametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and non-extreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.