A Cascading Bias Correction Method for Global Climate Model Simulated Multiyear Precipitation Variability

Abstract

Abstract The use of global climate model (GCM) precipitation simulations typically requires corrections for precipitation biases at subgrid spatial scales, typically at daily or monthly time scales. However, over many regions GCMs underestimate the magnitudes of multiyear precipitation extremes in the observed climate, resulting in a likely underestimation of the magnitudes of multiyear precipitation extremes in future scenarios. The objective of this study is to propose a method to extract from GCMs more realistic scenarios of multiyear precipitation extremes over time horizons of decades to one century. This proposed correction method is analogous to widely used bias correction methods, except that it is applied to variability at longer time scales than previous implementations (i.e., multiyear rather than daily or monthly). A case study of precipitation over a basin from the New York City water supply system demonstrates the potential magnitude of the underestimation of multiyear precipitation using uncorrected GCM scenarios, and the potential impact of the correction on multiyear hydrological extremes. Overall, it is a practical, conceptually simple approach meant for water supply system impact studies, but can be used for any impact studies that require more realistic multiyear extreme precipitation extreme scenarios. Significance Statement The purpose of this study is to present a practical method to address a particular difficulty that in some regions arises in climate change impact studies: global climate models tend to underestimate the multiyear variability of precipitation over some regions, resulting in an underestimation of the magnitudes and/or intensities of prolonged droughts as well as prolonged wet periods. The method is analogous to widely used bias correction methods, except it is applied to variability at longer time scales than previous implementations (i.e., multiyear rather than daily or monthly). It is designed to provide more realistic estimates of extreme hydrological scenarios during the twenty-first century. Our particular interest is for managers of water supply systems, but the method may be of interest to others for whom multiyear precipitation extremes are critical.