High‐resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical‐statistical approach

Abstract

We develop a dynamical–statistical downscaling approach by coupling the PRECIS regional modelling system and a statistical method—SCADS—to construct very high resolution climate projections for studying climate change impacts at local scales. The coupled approach performs very well in hindcasting the mean temperature of present‐day climate, while the performance for precipitation is relatively poor due to its high spatial variability and nonlinear nature but its spatial patterns are well captured. We then apply the coupled approach for projecting the future climate over the province of Ontario, Canada at a fine resolution of 10 km. The results show that there would be a significant warming trend throughout this century for the entire province while less precipitation is projected for most of the selected weather stations. The projections also demonstrate apparent spatial variability in the amount of precipitation but no noticeable changes are found in the spatial patterns.