One of the main challenges in climate change impact assessment studies is selecting climate change scenarios. By focusing on selecting projected extremes in a high dimensional space, one is confronted with the shrinkage of ensemble size while preserving the projection spread. This study proposes a novel integrated computational geometry algorithm to select extreme climate change scenarios in a high dimensional space. A set of 12 prominent climate extremes indices were used (as input to the algorithm) out of the 27 core indices recommended by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI). The ETCCDI indices were projected by Coupled Model Intercomparison Project Phase 5 (CMIP5) for the period of 2081–2100 relative to the baseline period 1986–2005. The approach enables the user to shrink the initial selected ensemble into smaller sub-ensembles while still capturing a wide range of simulated changes for selected climatological variables. The conservation of the projection spread was evaluated using a robust validation method when the spread error was calculated for each simulation run. The developed algorithm was applied to three different regions where the geographical domain was narrowed-down from sub-continental (western North America) to its regional (Alberta, Canada), and local (Athabasca River basin, Alberta, Canada) subdomains. Results revealed that selected extreme scenarios can vary from one region to another within the same geographical domain in response to the spatial variation in climatic regime.