Climatic Change

, Volume 115, Issue 3–4, pp 629–666 | Cite as

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia



This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.


  1. Chiew FHS, Teng J, Kirono D, Frost AJ, Bathols JM, Vaze J, Viney NR, Young WJ, Hennessy KJ, Cai WJ (2008) Climate data for hydrologic scenario modeling across the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project, CSIRO, Australia, p 35Google Scholar
  2. Chiew FHS, Teng J, Vaze J, Kirono DGC (2009) Influence of global climate model selection on runoff impact assessment. J Hydrol 379:172–180CrossRefGoogle Scholar
  3. Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change factor methods: impacts on lowflows in the river thames, united kingdom. Clim Chang 69:245–268CrossRefGoogle Scholar
  4. Dibike YB, Gachon P, St-Hilaire A, Ouarda TBMJ, Nguyen VTV (2008) Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada. Theor Appl Climatol 91:149–170CrossRefGoogle Scholar
  5. Dubrovsky M, Zalud Z, Stastna M (2000) Sensitivity of Ceres-Maize yields to statistical structure of daily weather series. Clim Chang 46:447–472CrossRefGoogle Scholar
  6. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578CrossRefGoogle Scholar
  7. Frost AJ, Mehrptra R, Sharma A, Srikanthan R (2009) Comparison of statistical downscaling techniques for multisite daily rainfall conditioned on atmospheric variables for the Sydney region. Aust J Water Resour 13:1–15Google Scholar
  8. Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O’Farrell SP, Waterman LJ, Hirst AC, Wilson SG, Collier MA, Watterson IG, Elliott TI (2002) The CSIRO Mk3 Climate System Model. CSIRO Atmospheric Research Technical Paper No. 60. (
  9. Gordon HB, O’Farrell SP, Collier MA, Dix MR, Rotstayn LD, Kowalczyk EA, Hirst AC, Waterman LJ, (2010) The CSIRO Mk3.5 Climate Model. CAWCR Technical Paper No.021. (
  10. Hewitson BC, Crane RG (2006) Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa. Int J Climatol 26:1313–1337CrossRefGoogle Scholar
  11. Jeffrey SJ, Carter JO, Moodie KM, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Software 16:309–330CrossRefGoogle Scholar
  12. Kanji GK (1996) 100 Statistical Tests. Sage Publications, London, p 216Google Scholar
  13. Katz RW (1996) Use of conditional stochastic models to generate climate change scenarios. Clim Chang 32:1573–1480CrossRefGoogle Scholar
  14. Khalili M, Leconte R, Brissette F (2007) Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation. J Hydrol 8:396–412Google Scholar
  15. Kilsby CG, Jones PD, Burton A, Ford AC, Fowler HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Software 22:1705–1719CrossRefGoogle Scholar
  16. Li Y, Smith I (2009) A Statistical Downscaling Model for Southern Australia Winter Rainfall. J Clim 22:1142–1158. doi:10.1175/2008JCLI2160.1 CrossRefGoogle Scholar
  17. Liu DL, Scott BJ (2001) Estimation of solar radiation in Australia from rainfall and temperature observations. Agric For Meteorol 106:41–59CrossRefGoogle Scholar
  18. Liu DL, Timbal B, Mo J, Fairweather H (2011) A GIS-based climate change adaptation strategy tool. Int J Clim Change Strat Man 3:140–155CrossRefGoogle Scholar
  19. Maxino CC, McAvaney BJ, Pitman AJ, Perkins SE (2007) Ranking the AR4 climate models over simulated maximum temperature, minimum temperature and precipitation. Int J Climatol 28:1097–1112CrossRefGoogle Scholar
  20. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multimodel data set: A new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  21. Mehrotra1 R, Sharma A (2010) Development and Application of a Multisite Rainfall Stochastic Downscaling Framework for Climate Change Impact Assessment. Water Resour Res 46 W07526, doi:10.1029/2009WR008423
  22. Mpelasoka FS, Mullan AB, Heerdegen RG (2001) New Zealand climate change information derived by multivariate statistical and artificial neural network approaches. Int J Climatol 21:1415–1433CrossRefGoogle Scholar
  23. Nakicenovic N, Swart R (eds) (2000) IPCC Special Report on Emissions Scenarios. Cambridge University Press, UKGoogle Scholar
  24. Overman OR, Gaines WL (1948) Linearity of regression of milk energy on fat percentage. J Anim Sci 7:55–59Google Scholar
  25. Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376CrossRefGoogle Scholar
  26. Pitman AJ, Perkins SE (2008) Regional Projections of Future Seasonal and Annual Changes in Rainfall and Temperature over Australia Based on Skill-Selected AR4 Models. Earth Internatl 12:1–50CrossRefGoogle Scholar
  27. Richardson CW, Wright DA (1984) WGEN: A Model for Generating Daily Weather Variables. U. S. Department of Agriculture, Agricultural Research Service, ARS-8, 83ppGoogle Scholar
  28. Sailor DJ, Li X (1999) A semiempirical downscaling approach for predicting regional temperature impacts associated with climate change. J Clim 12:103–114CrossRefGoogle Scholar
  29. Schoof JT, Pryor SC (2001) Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks. Int J Climatol 21:773–790CrossRefGoogle Scholar
  30. Schoof JT, Pryor SC, Robeson SM (2007) Downscaling daily maximum and minimum temperatures in the Midwestern USA: a hybrid empirical approach. Int J Climatol 27:439–454CrossRefGoogle Scholar
  31. Semenov MA (2008) Simulation of extreme weather events by a stochastic weather generator. Clim Res 35:203–212CrossRefGoogle Scholar
  32. Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Chang 35:397–414CrossRefGoogle Scholar
  33. Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107CrossRefGoogle Scholar
  34. Smith I, Chandler E (2010) Refining rainfall projections for the Murray Darling Basin of south-east Australia—the effect of sampling model results based on performance. Clim Chang 102:337–393. doi:10.1007/s10584-009-757-1 CrossRefGoogle Scholar
  35. Sun F, Roderick ML, Lim WH, Farquhar GD (2011) Hydroclimatic projections for the Murray Darling Basin based on an ensemble derived from Intergovernmental Panel on Climate Change AR4 climate models, Water Resour Res 47: W00G02, doi:10.1029/2010WR009829.
  36. Suppiah R, Hennessy KJ, Whetton PH, McInnes K, Macadam I, Bathols J, Ricketts J, Page CM (2007) Australian climate change projections derived from simulations performed for the IPCC 4th Assessment Report. Aust Met Mag 56:131–152Google Scholar
  37. Timbal B, Jones DA (2008) Future projections of winter rainfall in southeast Australia using a statistical downscaling technique. Clim Chang 86:165–187CrossRefGoogle Scholar
  38. Timbal B, Fernandez E, Li Z (2009) Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environ Model Software 24:341–359CrossRefGoogle Scholar
  39. Vrac M, Naveau P (2007) Stochastic downscaling of precipitation: From dry events to heavy rainfalls. Water Resour Res 43:W07402. doi:10.1029/2006WR005308 CrossRefGoogle Scholar
  40. Wilby RL, Dawson CW, Barrow EM (2002) SDSM - a decision support tool for the assessment of regional climate change impacts. Environ Model Software 17:147–159CrossRefGoogle Scholar
  41. Wilks DS (1992) Adapting stochastic weather generation algorithms for climate change studies. Clim Chang 22:67–84CrossRefGoogle Scholar
  42. Wilks DS (1999) Multisite downscaling of daily precipitation with a stochastic weather generator. Clim Res 11:125–136CrossRefGoogle Scholar
  43. Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107(D20):4429. doi:10.1029/2001JD00659 CrossRefGoogle Scholar
  44. Zhang XC (2005) Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agric For Meteorol 135:215–229CrossRefGoogle Scholar
  45. Zhang XC (2007) A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments. Clim Chang 84:337–363CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaAustralia
  2. 2.EH Graham Centre for Agricultural Innovation (An alliance between NSW Department of Primary Industries and Charles Sturt University)Wagga WaggaAustralia
  3. 3.NSW Office of WaterQueanbeyan NSW Office of WaterQueanbeyanAustralia

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