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Evaluating the Credibility of Downscaling: Integrating Scale, Trend, Extreme, and Climate Event into a Diagnostic Framework

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  • 1 School of Ecological and Environmental Sciences, and Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, and Institute of Eco-Chongming, Shanghai, China
  • 2 Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania
  • 3 Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pennsylvania
  • 4 School of Ecological and Environmental Sciences, and Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, and Institute of Eco-Chongming, Shanghai, China
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Abstract

Because downscaling tools are needed to support climate change mitigation and adaptation practices, the guarantee of their credibility is of vital importance. To evaluate downscaling results, one needs to select a set of effective and nonoverlapping indices that reflect key system attributes. However, this subject is still insufficiently researched. With this study, we propose a diagnostic framework that evaluates the credibility of precipitation downscaling using five different attributes: spatial, temporal, trend, extreme, and climate event. A daily variant of the bias-corrected spatial downscaling approach is used to downscale daily precipitation from the GFDL-ESM2G climate model at 148 stations in the Yangtze River basin in China. Results prove that this framework is effective in systematically evaluating the performance of downscaling across the Yangtze River basin in the context of climate change and exacerbating climate extremes. Moreover, results also indicate that the downscaling approach adopted in this study yields good performance in correcting spatiotemporal bias, preserving trends, approximating extremes, and characterizing climate events across the Yangtze River basin. The proposed framework can be beneficial to planners and engineers facing issues relevant to climate change assessment.

Corresponding author: Yue Che, yche@des.ecnu.edu.cn

Abstract

Because downscaling tools are needed to support climate change mitigation and adaptation practices, the guarantee of their credibility is of vital importance. To evaluate downscaling results, one needs to select a set of effective and nonoverlapping indices that reflect key system attributes. However, this subject is still insufficiently researched. With this study, we propose a diagnostic framework that evaluates the credibility of precipitation downscaling using five different attributes: spatial, temporal, trend, extreme, and climate event. A daily variant of the bias-corrected spatial downscaling approach is used to downscale daily precipitation from the GFDL-ESM2G climate model at 148 stations in the Yangtze River basin in China. Results prove that this framework is effective in systematically evaluating the performance of downscaling across the Yangtze River basin in the context of climate change and exacerbating climate extremes. Moreover, results also indicate that the downscaling approach adopted in this study yields good performance in correcting spatiotemporal bias, preserving trends, approximating extremes, and characterizing climate events across the Yangtze River basin. The proposed framework can be beneficial to planners and engineers facing issues relevant to climate change assessment.

Corresponding author: Yue Che, yche@des.ecnu.edu.cn
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