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Current building energy use is projected to increase by 1.7% annually until 2025, and the great potential for energy reduction in existing buildings has created opportunities in building energy retrofit projects. In this research, a framework and method are proposed to evaluate the impacts of different retrofit options to existing building under climate change. A Python retrofit tool is developed to perform parametric study by running EnergyPlus under different retrofit scenarios for existing buildings. With the help of Latin-hypercube sampling (LHS) method and a joint mutual information maximization (JMIM)-based feature selection method, the energy conservation measure (ECM) that may have the most potential in reducing the energy use or the lifecycle net present value (NPV) of a target existing building can be selected. A validated data-driven model is used to predict the building's future hourly energy use based on EnergyPlus simulation results generated by future extreme year weather data. It is demonstrated that global climate change will alter the optimal solution of future ECM combination and its influence varies from building to building, location to location. The optimal retrofit strategy of selecting the best ECM combinations under current climate condition will be subject to change in the future climate condition.