SDSM–A Decision Support Tool for the Assessment of Regional Climate Change Impacts. General Circulation Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called ‘downscaling’ techniques are used to bridge the spatial and temporal resolution gaps between what climate modellers are currently able to provide and what impact assessors require. This paper describes a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. Statistical DownScaling Model (sdsm) facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. Additionally, the software performs ancillary tasks of predictor variable pre-screening, model calibration, basic diagnostic testing, statistical analyses and graphing of climate data. The application of sdsm is demonstrated with respect to the generation of daily temperature and precipitation scenarios for Toronto, Canada by 2040–2069

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  1. Ermoliev, Yu.; Ermolieva, T.; Kahil, T.; Obersteiner, M.; Gorbachuk, V.; Knopov, P.: Stochastic optimization models for risk-based reservoir management (2019)
  2. Ma, Yonggang; Huang, Yue; Chen, Xi; Li, Yongping; Bao, Anming: Modelling snowmelt runoff under climate change scenarios in an ungauged mountainous watershed, Northwest China (2013) ioport
  3. He, Xuming; Yang, Yunwen; Zhang, Jingfei: Bivariate downscaling with asynchronous measurements (2012)
  4. Dibike, Yonas B.; Coulibaly, Paulin: Temporal neural networks for downscaling climate variability and extremes. (2006) ioport