Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

Année de publication

2026

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale 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 the 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 is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (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 nonparametric 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 nonextreme 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. Significance Statement High-resolution precipitation data generated from dynamical downscaling (DD) or statistical downscaling (SD) have played an essential role in regional climate, hydrologic, and ecological studies. However, there is no consistent evaluation framework to understand their absolute and relative performance. Meanwhile, increasing use of these data for different purposes necessitates the development of an evaluation framework that examines both traditional grid-based and event-based precipitation features to identify high-quality precipitation data for impact studies, such as assessment of flood hazards. In this study, we evaluated 8 DD and 3 SD products, a total of 78 datasets, under the same framework. This collection includes some of the most popular SD datasets [localized constructed analogs, version 2 (LOCA2); multivariate adaptive constructed analogs, version 2 (MACAv2); seasonal trends and analysis of residuals-empirical statistical downscaling model, version 1 (STAR-ESDMv1)] along with recently available high-resolution DD products such as 4-km long-term regional hydroclimate reanalysis over the contiguous United States (CONUS404) and Argonne downscaled data archive, version 2 (ADDA2). We analyzed the quality of precipitation objects in these datasets using four features (storm total precipitation, precipitation area, peak intensity, spatial structure) for 17 CONUS regions and four seasons using three metrics. This comprehensive evaluation can inform users of the relative advantages of each dataset, guiding them toward the selection of datasets that meet their specific needs.</div>

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