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ABSTRACT The objective of this study is to investigate the vulnerability of different land-cover types to climate change. To this end, land-cover specific temperature change factors are quantified for the southern Saskatchewan region using a novel statistical downscaling model: physical scaling ( SP). SP model considers large-scale climate and regional physical characteristics like land-cover, elevation in its formulation and hence can be used to predict future temperature for different land-cover types under changing large-scale climatic and land-cover conditions. The model is validated by assessing its ability to downscale North American Regional Reanalysis ( NARR) derived surface (skin) temperature from an initial resolution of 32 km to 500 m. The downscaled NARR data are evaluated using a cross-validation approach over the period 2006-2013 with reference to MODerate-resolution Imaging Spectroradiometer ( MODIS) derived surface temperature estimates and satisfactory model performance is obtained (average RMSE = 0.03 K). The validated model is used to predict future surface temperature across the study region. Future land-cover projections are derived by downscaling land-use projections for Representative Concentration Pathways ( RCPs) 2.6 and 8.5 made by integrated assessment models: IMAGE and MESSAGE, respectively. An analysis of land-cover specific temperature changes between historical (2006-2013) and future (2081-2100) timelines indicate variations of up to 2 K between different land-cover classes. Vulnerability pattern of different land-cover classes differ significantly between day- and night-time. Further, variations of upto 1 K in projected changes are observed among different forest cover types. Closed shrubland is obtained as the most vulnerable forest-cover class whereas evergreen broadleaf forest is found to be the least vulnerable. [ABSTRACT FROM AUTHOR]