Characterizing and correcting for global climate models’ biases in multiyear extreme precipitation scenarios

Allan Frei (), Rakesh Gelda and Rajith Mukundan
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Allan Frei: Hunter College, City University of New York, Department of Geography and Environmental Science
Rakesh Gelda: Bureau of Water Supply, New York City Department of Environmental Protection
Rajith Mukundan: Bureau of Water Supply, New York City Department of Environmental Protection

Climatic Change, 2025, vol. 178, issue 12, No 7, 26 pages

Abstract: Abstract A number of studies have demonstrated a tendency for Global Climate Models (GCMs) to underestimate multiyear precipitation variability across many regions of the globe. We present the Multiyear Precipitation Variability Bias Correction (MPVBC) method to provide more realistic multiyear extreme precipitation scenarios. Our case study includes the basins encompassing the portion of the New York City water supply system located west of the Hudson River, which historically provide ninety percent of the City’s water. In this region, multiyear precipitation variability, defined as the range in precipitation, is underestimated in GCMs by factors between approximately 1.5 and 7 for 1–20 year durations, with median GCMs underestimating variability by a factor of between two and three. The MPVBC correction changes median GCM precipitation magnitude during the most extreme dry and wet events by 10% to 25%. These results include GCMs that have been subjected to widely-used downscaling and/or bias correction techniques at annual or sub-annual timescales. This suggests that GCM-derived future multiyear extreme precipitation scenarios may not represent the full range of likely extreme scenarios, potentially hindering evaluations of water supply system resiliency, or even worse, giving water managers unrealistic complacency.

Keywords: Climate; Precipitation; Multiyear variability; Bias correction; Drought; Pluvial (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10584-025-04033-z

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