• Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004: Can climate trends be calculated from reanalysis data? J. Geophys. Res., 109, D11111, doi:10.1029/2004JD004536.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher, 2008: On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys. Res. Lett., 35, L20709, doi:10.1029/2008GL035694.

    • Search Google Scholar
    • Export Citation
  • Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics, Springer, 228 pp.

  • Das, T., A. Bardossy, E. Zehe, and Y. He, 2008: Comparison of conceptual model performance using different representations of spatial variability. J. Hydrol., 356, 106118.

    • Search Google Scholar
    • Export Citation
  • Eden, J., M. Widmann, D. Grawe, and S. Rast, 2012: Skill, correction, and downscaling of GCM-simulated precipitation. J. Climate, 25, 39703984.

    • Search Google Scholar
    • Export Citation
  • Hay, L. E., and M. P. Clark, 2003: Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States. J. Hydrol., 282, 5675.

    • Search Google Scholar
    • Export Citation
  • Jacob, D., 2001: A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteor. Atmos. Phys., 77 (1–4), 6173.

    • Search Google Scholar
    • Export Citation
  • Kallache, M., M. Vrac, P. Naveau, and P.-A. Michelangeli, 2011: Nonstationary probabilistic downscaling of extreme precipitation. J. Geophys. Res., 116, D05113, doi:10.1029/2010JD014892.

    • Search Google Scholar
    • Export Citation
  • Maraun, D., 2012: Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys. Res. Lett., 39, L06706, doi:10.1029/2012GL051210.

    • Search Google Scholar
    • Export Citation
  • Maraun, D., and Coauthors, 2010: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.

    • Search Google Scholar
    • Export Citation
  • Piani, C., J. O. Haerter, and E. Coppola, 2010: Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol., 99 (1–2), 187192.

    • Search Google Scholar
    • Export Citation
  • Thorne, P. W., and R. S. Voss, 2010: Reanalysis suitable for characterizing long-term trends. Are they really achievable? Bull. Amer. Meteor. Soc., 91, 353361.

    • Search Google Scholar
    • Export Citation
  • van der Linden, P., and J. F. B. Mitchell, 2009: ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre Tech. Rep., 160 pp.

  • von Storch, H., 1999: On the use of “inflation” in statistical downscaling. J. Climate, 12, 35053506.

  • Xu, C., 1999: From GCMs to river flow: A review of downscaling methods and hydrologic modelling approaches. Prog. Phys. Geogr., 23, 229.

    • Search Google Scholar
    • Export Citation
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Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

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  • 1 GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
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Abstract

Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

Corresponding author address: Douglas Maraun, GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany. E-mail: dmaraun@geomar.de

A comment/reply has been published regarding this article and can be found at http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00184.1, http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0362.1, and http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0592.1

Abstract

Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

Corresponding author address: Douglas Maraun, GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany. E-mail: dmaraun@geomar.de

A comment/reply has been published regarding this article and can be found at http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00184.1, http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0362.1, and http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0592.1

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