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Abstract

It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.