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Background: Phlebotomus pedifer is the vector for Leishmania aethiopica causing cutaneous leishmaniasis (CL) in southwestern Ethiopia. Previous research on the transmission dynamics of CL resulted in recommendations for vector control. In order to target these interventions towards affected areas, a comprehensive understanding of the spatial distribution of P. pedifer at high spatial resolution is required. Therefore, this study determined the environmental predictors that facilitate the distribution of P. pedifer and created a map indicating the areas where conditions are suitable for survival of the vector in southwestern Ethiopia with high spatial resolution., Methods: Phlebotomus pedifer presence points were collected during two entomological surveys. Climate, vegetation and topographic variables were assembled. Climate variables were interpolated with variables derived from high-resolution digital elevation models to generate topoclimatic layers representing the climate conditions in the highlands. A Maximum Entropy model was run with the presence points, predicting variables and background points, which were selected based on a bias file., Results: Phlebotomus pedifer was the only captured Phlebotomus species in the study area and was collected at altitudes ranging between 1685 and 2892 m. Model projections indicated areas with suitable conditions in a 'belt' surrounding the high mountain peaks. Model performance was high, with train and test AUC values being 0.93 and 0.90, respectively. A multivariate environmental similarity surface (MESS) analysis showed that the model projection was only slightly extrapolated for some of the variables. The mean annual temperature was the environmental variable, which contributed most to the model predictions (60.0%) followed by the seasonality in rainfall (13.2%). Variables representing steep slopes showed very low importance to model predictions., Conclusions: Our findings indicate that the suitable habitats for P. pedifer correspond well with the altitudes at which CL was reported previously, but the predictions are more widely distributed, in contrast with the description of CL to occur in particular foci. Moreover, we confirm that vector distribution is driven by climate factors, suggesting inclusion of topoclimate in sand fly distribution models. Overall, our model provides a map with a high spatial resolution that can be used to target sand fly control measures in southwestern Ethiopia.