Climate change has altered regular temperature patterns and various climate variables on a global scale, causing growing concerns about future food, water and energy security. Immediate action should be taken to understand the extent of climate change while also proposing adaptation strategies to cope with the projected future climate conditions. From the energy security perspective, particularly in consideration of the ever-increasing human population, an important aspect to understand is the impact of climate change on the energy consumption of residential buildings. Understanding the impact of climate change on building energy consumption is not only beneficial for advising efficient energy-saving measures, but also for understanding future energy requirements. Various studies have already shown that climate change effects heating and cooling loads of buildings in various climates around the world. However, there is a lack of comprehension of the effects of climate change on energy consumption in Quebec. In addition, some of the methodologies employed to address the impact of climate change in buildings may be not be accurate or accessible to practitioners. The present study tries to fill this gap by advising a simple procedure that can be implemented in day-to-day engineering practice for a detailed understanding of the effects of climate change on building energy consumption. The methodology is applied to a residential building in Montreal, Quebec (Canada), using the state-of-the-art climate model projections for the periods of 2011-2040 (short-term future), 2041-2070 (midterm future), and 2071-2100 (long-term future) and under low and high greenhouse gas concentration scenarios. In brief, the available projections of five global climate models was studied for two particular weather parameters, namely dry-bulb temperature and shortwave radiation. The projections were downscaled at the point location and at an hourly resolution using a cascade model based on a quantile mapping bias correction method and a modified quantile-based k-nearest neighbor method. The downscaled projections were used as inputs to TRNSYS, an energy simulation software, in order to quantify the heating and cooling loads as well as judge the overall performance of the residential building in Montreal. This methodology can provide a basis for detailed understanding of the impacts of climate change on building energy consumption. Considering the applied case study, it is understood that climate change will not only change the intensity of the heating and cooling loads but can also change the empirical distribution of hourly energy consumption, particularly during peak loads.