Climatic Change

, Volume 112, Issue 2, pp 449–468 | Cite as

Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

  • Matthias Jakob ThemeßlEmail author
  • Andreas Gobiet
  • Georg Heinrich


Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.


Regional Climate Model Precipitation Amount Pacific Decadal Oscillation Quantile Mapping Climate Change Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged especially the CCLM simulations provided by the ETH Zurich. We furthermore acknowledge the E-OBS dataset from the ENSEMBLES project and the data providers in the ECA&D project ( as well as the EU FP6 project CLAVIER which partly funded this study.

Supplementary material

10584_2011_224_Fig11_ESM.gif (111 kb)
Fig. S1

Annual bias characteristics of pint (top row), pn10 (middle row), and px1d (bottom row). Left column: uncorrected model; right column: corrected model. (GIF 111 kb)

10584_2011_224_MOESM1_ESM.eps (8.5 mb)
High resolution image file (EPS 8722 kb)
10584_2011_224_Fig12_ESM.gif (77 kb)
Fig. S2

The same as in Fig. S1 but for tasx (top row), txn25 (middle row) and tnn20 (bottom row). (GIF 77 kb)

10584_2011_224_MOESM2_ESM.eps (8.2 mb)
High resolution image file (EPS 8441 kb)
10584_2011_224_Fig13_ESM.gif (54 kb)
Fig. S3

Seasonal pdfs of precipitation amount (first row) and of mean temperature (third row) for the period 1971–2000 (dashed light grey) and 2021–2050 (black). The lower part of each panel displays the differences between scenario and control period at different percentiles. The second and fourth rows show the seasonal precipitation and temperature correction functions. Correction terms are derived from differences at different percentiles between observed and modeled ecdfs. The regional mean quantities corresponding to these percentiles are indicated on the respective x-axes. Results are shown for sub-region AL. (GIF 54 kb)

10584_2011_224_MOESM3_ESM.eps (282 kb)
High resolution image file (EPS 281 kb)
10584_2011_224_Fig14_ESM.gif (97 kb)
Fig. S4

Annual mean maps of the uncorrected monthly CCS (left column), the difference between the uncorrected and the corrected CCS (middle column), and the respective annual cycles of the CCS for three sub-regions. Top row: pint; middle row: pn10; bottom row: px1d. In the lower part of the annual cycle plots change of significance is indicated with “o” (unchanged significance), “-” (loss of significance after correction), and “+” (significance established after correction). (GIF 96 kb)

10584_2011_224_MOESM4_ESM.eps (8.3 mb)
High resolution image file (EPS 8471 kb)


  1. Bardossy A, Pegram G (2011) Downscaling precipitation using regional climate models and circulation patterns toward hydrology. Water Resour Res 47:W04505. doi: 10.1029/2010WR009689 CrossRefGoogle Scholar
  2. Benestad RE, Hanssen-Bauer I, Chen D (2008) Empirical statistical downscaling. World Scientific Publishing Company, New JerseyGoogle Scholar
  3. Boé J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int J Climatol 27:1643–1655CrossRefGoogle Scholar
  4. Böhm U, Kücken M, Ahrens W, Block A, Hauffe D, Keuler, K Rockel B, Will A (2006) Clm—the climate version of lm: brief description and long-term applications. COSMO Newsletter, 6Google Scholar
  5. Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Chang 81:7–30. doi: 10.1007/s10584-006-9210-7 CrossRefGoogle Scholar
  6. Déqué M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Glob Planet Chang 57:16–26CrossRefGoogle Scholar
  7. Dettinger MD, Cayan DR, Meyer MK, Jeton AE (2004) Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American river basins, Sierra Nevada, California, 1900–2099. Clim Chang 62:283–317CrossRefGoogle Scholar
  8. Dobler A, Ahrens B (2008) Precipitation by a regional climate model and bias correction in Europe and South Asia. Meteorol Z 17:499–509CrossRefGoogle Scholar
  9. Fowler HJ, Kilsby CG (2007) Using regional climate model data to simulate historical and future river flows in northwest England. Clim Chang 80:337–367CrossRefGoogle Scholar
  10. Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res 108(D3):4124. doi: 10.1029/2002JD002287 CrossRefGoogle Scholar
  11. Giorgi F, Coppola E (2007) European climate-change oscillation (ECO). Geophys Res Lett 34:L21703. doi: 10.1029/2007GL031223 CrossRefGoogle Scholar
  12. Giorgi F, Mearns LO (1991) Approaches to the simulation of regional climate change: a review. Rev Geophys 29:191–216CrossRefGoogle Scholar
  13. Giorgi F, Mearns LO (1999) Introduction to special section: regional climate modelling revisited. J Geophys Res 104:6335–6352CrossRefGoogle Scholar
  14. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  15. Graham LP, Andréasson J, Carlsson B (2007) Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods—a case study on the Lule River basin. Clim Chang 81:293–307CrossRefGoogle Scholar
  16. Gutowski WJ, Decker SG, Donavon RA, Pan Z, Arritt RW, Takle ES (2003) Temporal-spatial scales of observed and simulated precipitation in central U.S. climate. J Clim 16:3841–3847CrossRefGoogle Scholar
  17. Hagemann S, Machenhauer B, Jones R, Christensen OB, Déqué M, Jacob D, Vidale PL (2004) Evaluation of water and energy budgets in regional climate models applied over Europe. Clim Dyn 23:547–567CrossRefGoogle Scholar
  18. Hagemann S, Chen C, Haerter JO, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol, in pressGoogle Scholar
  19. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J Geophys Res 113:1–12, D20119. doi: 10.1029/2008JD10201 Google Scholar
  20. Heinrich G, Gobiet A (2011) The future of dry and wet spells in Europe: a comprehensive study based on the ENSEMBLES regional climate models. Int J Climatol, (in press)Google Scholar
  21. Hofstra N, Haylock M, New M, Jones PD (2009) Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. J Geophys Res 114:D21101. doi: 10.1029/2009JD011799 CrossRefGoogle Scholar
  22. Jacob D, Bärring L, Christensen OB, Christensen JH, de Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SI, Somot S, van Ulden A, van den Hurk B (2007) An inter-comparison of regional climate models for Europe: design of the experiments and model performance. Clim Chang 81:31–52. doi: 10.1007/s10584-006-9213-4 CrossRefGoogle Scholar
  23. Leander R, Buishand TA (2007) Resampling of regional climate model output for the simulation of extreme river flows. J Hydrol 332:487–496CrossRefGoogle Scholar
  24. Leander R, Buishand TA, van den Hurk BJJM, de Wit MJM (2008) Estimated changes in flood quantiles of the river Meuse from resampling of regional climate models. J Hydrol 351:331–343CrossRefGoogle Scholar
  25. Lenderik G, Buishand A, van Deursen W (2007) Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrol Earth Syst Sci 11:1145–1159CrossRefGoogle Scholar
  26. Maraun D, Ireson A, Wetterhall F, Bachner S, Kendon E, Rust HW, Venema VKC, Widmann M, Chandler RE, Onof CJ, Osborn TJ, Sautner T, Themeßl M, Thiele-Eich I (2010) Statistical downscaling and modelling of precipitation. Bridging the gab between dynamical models and the end users. Rev Geophys 48:RG3003. doi: 10.1029/2009RG000314 CrossRefGoogle Scholar
  27. Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) IPCC Special Report on Emissions Scenarios. Cambridge University Press, CambridgeGoogle Scholar
  28. Piani C, Haerter JO, Coppola E (2009) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol. doi: 10.1007/s00704-009-0134-9
  29. Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol. doi: 10.1016/j.jhydrol.2010.10.024
  30. Rojas R, Feyen L, Dosio A, Bavera D (2011) Improving pan-european hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations. Hydrol Earth Syst Sci Discuss 8:3883–3936CrossRefGoogle Scholar
  31. Salathé EP (2005) Downscaling simulations of future global climate with application to hydrologic modeling. Int J Climatol 25:419–436CrossRefGoogle Scholar
  32. Srikanthan R, Pegram G (2009) A nested multisite daily rainfall stochastic model generator. J Hydrol 371:142–153CrossRefGoogle Scholar
  33. Suklitsch M, Gobiet A, Leuprecht A, Frei C (2008) High resolution sensitivity studies with the regional climate model CCLM in the Alpine Region. Meteorol Z 17:467–476CrossRefGoogle Scholar
  34. Suklitsch M, Gobiet A, Truhetz H, Awan NK, Göttel H, Jacob D (2010) Error characteristics of high resolution regional climate models over the Alpine area. Clim Dyn. doi: 10.1007/s00382-010-0848-5
  35. Themeßl M, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1531–1544. doi: 10.1002/joc.2168 Google Scholar
  36. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, Da Costa BV, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, van De Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, Mcnally AP, Mahfouf JF, Morcrette JJ, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131(612):2961–3012CrossRefGoogle Scholar
  37. van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, ExeterGoogle Scholar
  38. van Pelt SC, Kabat P, ter Maat HW, van den Hurk BJJM, Weerts AH (2009) Discharge simulation performed with a hydrological model using bias corrected regional climate model input. Hydrol Earth Syst Sci 13:2387–2397CrossRefGoogle Scholar
  39. Wang Y, Leung LR, McGregor JL, Lee DK, Wang WC, Ding Y, Kimura F (2004) Regional climate modeling: progress, challenges, and prospects. J Meteorol Soc Japan 82:1599–1628CrossRefGoogle Scholar
  40. Wilks DS (1995) Statistical methods in atmospheric science, volume 59 of International Geophysics Series. Academic, San DiegoGoogle Scholar
  41. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscale climate model outputs. Clim Chang 62:189–216CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Matthias Jakob Themeßl
    • 1
    Email author
  • Andreas Gobiet
    • 1
  • Georg Heinrich
    • 1
  1. 1.Wegener Center for Climate and Global Change and Institute for Geophysics, Astrophysics, and MeteorologyUniversity of GrazGrazAustria

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