Species distributions models (SDMs) are increasingly used to predict species' potential range shift or extinction risk in response to future climate change. Here, we used the ensemble predictions of the models in order to estimate the impact of climate change on the geographical distribution of Juniperus excelsa M. Bieb. in Chaharmahal va Bakhtiari province in the Central Zagros region, Iran. We projected climate change impacts for 2050 based on four scenarios of the increase in the greenhouse gases in the general circulation model MRI-CGCM3. We then used the bioclimatic and topographic variables to create a model ensemble from six different SDM algorithms including Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM), Multivariate Adaptive Regression Splines (MARS), and Random Forest (RF). The findings indicated that 26.5% of the study area (4393.98 km2) is suitable for the J. excelsa. Annual precipitation, slope and Isothermality had the highest overall contribution to model performance. Based on the different climate change scenarios, 36.63% to 77.63% of the species' suitable habitats will be loss by 2050. These results can provide reliable information on preparing adaptive responses for the sustainable management of the species.