Theoretical and Applied Climatology

, Volume 120, Issue 1–2, pp 377–390 | Cite as

An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran

  • M. FarajzadehEmail author
  • R. Oji
  • A. J. Cannon
  • Y. Ghavidel
  • A. Massah Bavani
Original Paper


Seven single-site statistical downscaling methods for daily temperature and precipitation, including four deterministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model-based recursive partitioning (MOB)] and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model–Decision Centric (SDSM–DC] are evaluated at nine stations located in the mountainous region of Iran’s Midwest. The methods are of widely varying complexity, with input requirements that range from single-point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981–2000 is used for model calibration and 2001–2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov–Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. MOB performed best, passing 14.5 % (49.6 %) of the combined (single) tests, respectively, followed by SDSM, CaDENCE, and GLM [14.5 % (46.5 %), 13.2 % (47.1 %), and 12.8 % (43.2 %), respectively], and then by QMD, CDFt, and ANM [7 % (45.7 %), 4.9 % (45.3 %), and 1.6 % (37.9 %), respectively]. Correlation tests were passed less frequently than KS tests. All methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.


Statistical Downscaling Quantile Mapping Weather Typing Statistical Downscaling Model Statistical Downscaling Method 
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.



This research project would not have been possible without the facilities and research environment of the Pacific Climate Impact Consortium (PCIC). Therefore, we are grateful to Prof. Francis Zwiers, PCIC director, who provided us with the opportunity to use these facilities.


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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • M. Farajzadeh
    • 1
    Email author
  • R. Oji
    • 1
  • A. J. Cannon
    • 2
  • Y. Ghavidel
    • 1
  • A. Massah Bavani
    • 3
  1. 1.Tarbiat Modares UniversityTehranIran
  2. 2.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada
  3. 3.Department of Irrigation and Drainage Engineering, College of AbouraihanUniversity of TehranPakdashtIran

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