ORIGINAL PAPER An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran M. Farajzadeh & R. Oji & A. J. Cannon & Y. Ghavidel & A. Massah Bavani Received: 7 July 2013 /Accepted: 9 April 2014 # Springer-Verlag Wien 2014 Abstract Seven single-site statistical downscaling methods for daily temperature and precipitation, including four deter- ministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribu- tion function transform (CDFt), and model-based recursive partitioning (MOB)] and three stochastic algorithms [general- ized 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 multivar- iate synoptic-scale fields. The period 1981–2000 is used for model calibration and 2001–2010 for validation, with perfor- mance 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 perfor- mance varied widely across the study region. 1 Introduction Over the past two decades, the analysis of climate extremes has become increasingly important due to the recognition of their significant impacts on society and natural systems (IPCC 2012). Many different approaches have been used to assess the effects of global warming on climate extremes (Kharin and Zwiers 2000; Tebaldi et al. 2006; Kharin et al. 2007; Min et al. 2011; Sillmann et al. 2013). Given the relatively coarse spatial discretization of global climate models (GCMs) and regional climate models (RCMs), statistical downscaling models are used to investigate climate extremes and to obtain probabilis- tic projections of climate change at a specific location (e.g., the site of a climate observing station). Statistical downscaling techniques bridge the gap in spatial resolution between what climate modelers are currently able to provide and what im- pact assessments require (Wilby and Dawson 2007). Several studies have investigated the impact of climate change on extreme values using a variety of downscaling methods (Schubert and Henderson-Sellers 1997; Olsson et al. 2001; Harpham and Wilby 2005; Dibike and Coulibaly 2006; Vrac and Naveau 2007; Benestad 2010; Kallache et al. 2011; Yang et al. 2012). The concept of downscaling is appealing because the added detail could inform site-specific assessment and management of climate risk. However, the extent to which the downscaling community is delivering these practical M. Farajzadeh (*) : R. Oji : Y. Ghavidel Tarbiat Modares University, Tehran, Iran e-mail: farajzam@modares.ac.ir R. Oji e-mail: ruhollah.oji@gmail.com Y. Ghavidel e-mail: ghavidel@modares.ac.ir A. J. Cannon Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada e-mail: acannon@uvic.ca A. Massah Bavani Department of Irrigation and Drainage Engineering, College of Abouraihan, University of Tehran, Pakdasht, Iran e-mail: armassah@ut.ac.ir Theor Appl Climatol DOI 10.1007/s00704-014-1157-4