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BackgroundClimate change is a significant and long-term change in the weather patterns over periods ranging from decades to millions of years. The impacts of climate change have been drawn more and more worldwide attention. To study the impacts, general circulation models (GCMs) were developed to simulate climate change at a global scale. The climate information obtained from GCMs is usually at a fairly coarse resolution. In comparison, regional climate models (RCMs) work in a small area of interest and can provide climate information at resolution as fine as 25 - 50 km. When higher resolution climate information is needed and the applied RCMs are incapable of undertaking the task, statistical downscaling techniques can be introduced to acquire the desired climate information. In this study, three interpolation methods are applied to downscale regional climate model (RCM) results for higher resolution climate information at 10 km.ResultsThe results indicated that the three interpolation methods could generate high-quality estimates at 10 km grids. The downscaled RCM results approximated to the 10 km official data which was published by Agriculture and Agri-Food Canada, Government of Canada. Compared with the results of IDW and spline methods, the results obtained from kriging method generated smoother interpolation map and showed modest variations in the difference map.ConclusionsAll the three interpolation methods could fulfill the task of downscaling the RCM results from 25 km to 10 km. Overall, kriging interpolation method showed better performance than the other two methods in this study.

Application of GIS in downscaling regional climate model results over the province of Ontario