INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. (2012) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3603 An asynchronous regional regression model for statistical downscaling of daily climate variables Anne M. K. Stoner, a * Katharine Hayhoe, a,b,c Xiaohui Yang b and Donald J. Wuebbles d a Climate Science Center, Texas Tech University, Lubbock, TX, USA b ATMOS Research & Consulting, Lubbock, TX, USA c Department of Political Science, Texas Tech University, Lubbock, TX, USA d Department of Atmospheric Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA ABSTRACT: The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere–ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling daily minimum and maximum temperature and precipitation for 20 stations in North America from diverse climate zones. Using cross-validation to maximize the independent comparison period, historical downscaled simulations are evaluated relative to observations in terms of three different quantities: the probability distributions, giving a visual image of the skill of each model; root-mean-square errors; and bias in nine quantiles that represent both means and extremes. Successive versions of the model show improved accuracy in simulating extremes, where AOGCMs are often most biased and which are frequently the focus of impact studies. Overall, the quantile regression-based technique is shown to be efficient, robust, and highly generalizable across multiple variables, regions, and climate model inputs. Copyright  2012 Royal Meteorological Society KEY WORDS statistical downscaling; quantile regression; climate; temperature; precipitation Received 29 April 2012; Revised 30 August 2012; Accepted 2 September 2012 1. Introduction Atmosphere–ocean general circulation models (AOGCMs) and the new generation of earth sys- tem models provide insights into the dynamic nature of possible climate responses to anthropogenic forcing. With spatial scales typically on the order of one half degree or coarser, however, they are unable to simulate climate at the local to regional scale. To compensate for this relatively coarse resolution, a number of dynamical and statistical techniques have been developed to downscale climate model outputs to the impact-relevant spatial and temporal scales at which observations are made. Despite the plethora of downscaling methods in the literature (Crane and Hewitson, 1998; Wilby et al ., 1998; Huth et al ., 2001; Stehlik and Bardossy, 2002; Wood et al ., 2004; Haylock et al ., 2006; Schmidli et al ., 2006; Kostopoulou et al ., 2007; Hidalgo et al ., 2008; to name just a few out of hundreds), relatively few downscaling methods have been applied to quantify potential impacts * Correspondence to: A. M. K. Stoner, Climate Science Center, Texas Tech University, 113 Holden Hall, Boston & Akron Streets, Lubbock, TX 79409-1015, USA. E-mail: anne.stoner@ttu.edu of climate change at the local to regional scale for a broad cross-section of regions and sectors across North America. The majority of studies of climate change impacts in the United States, for example, rely on one of five methods: a delta approach whereby a change or ‘delta’ is added to observed mean annual, seasonal, or monthly values in order to get future values (Hay et al ., 2000; as used in USGCRP, 2000); simulations from a regional climate model (e.g. Mearns et al ., 2009; as used in NARCCAP); the Bias Correction-Statistical Downscaling model originally developed as a front end to the hydrological variable infiltration capacity model, which uses a quantile mapping approach to downscale monthly AOGCM-based temperature and precipitation to a regular grid (Wood et al ., 2004; as used in Hayhoe et al ., 2004, 2008; Luers et al ., 2006; USGCRP, 2009); a constructed analogue approach that matches AOGCM- simulated patterns to historical weather patterns (Hidalgo et al ., 2008; as used in Luers et al ., 2006); and a linear asynchronous regression approach that downscales daily AOGCM-based temperature and precipitation to individual station locations (Dettinger et al ., 2004; as used in Hayhoe et al ., 2004, 2008, 2010). Each of these methods has its own benefits, and each can be sufficient for certain applications. For example, Copyright  2012 Royal Meteorological Society