Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2649-2017
https://doi.org/10.5194/hess-21-2649-2017
Research article
 | 
06 Jun 2017
Research article |  | 06 Jun 2017

Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

Matthew B. Switanek, Peter A. Troch, Christopher L. Castro, Armin Leuprecht, Hsin-I Chang, Rajarshi Mukherjee, and Eleonora M. C. Demaria

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Cited articles

Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small, Water Resour. Res., 48, W09502, https://doi.org/10.1029/2011WR011524, 2012.
Berg, A. A., Famiglietti, J. S., Walker, J. P., and Houser, P. R.: Impact of bias correction to reanalysis products on simulations of north american soil moisture and hydrological fluxes, J. Geophys. Res., 108, 4490, https://doi.org/10.1029/2002JD003334, 2003.
Boé, J., Terray, L., Habets, F. and Martin, E.: Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies, Int. J. Climatol., 27, 1643–1655, https://doi.org/10.1002/joc.1602, 2007.
Brekke, L., Thrasher, B., Maurer, E. P., and Pruitt, T.: Downscaling CMIP3 and CMIP5 climate projections: Release of downscaled CMIP5 climate projections, comparison with preceding information, and summary of user needs, US Dept. of Interior, Bureau of Reclamation, Denver, CO, 104 pp., 2013.
Bürger, G., Sobie, R., Cannon, A. J., Werner, A. T., and Murdock, T. Q.: Downscaling extremes: An intercomparison of multiple methods for future climate, J. Climate, 26, 3429–3449, https://doi.org/10.1175/JCLI-D-12-00249.1, 2013.
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Short summary
The commonly used bias correction method called quantile mapping assumes a constant function of error correction values between modeled and observed distributions. Our article finds that this function cannot be assumed to be constant. We propose a new bias correction method, called scaled distribution mapping, that does not rely on this assumption. Furthermore, the proposed method more explicitly accounts for the frequency of rain days and the likelihood of individual events.