Ecological niche models, crucial for estimating species’ potential distribution under global change, can face reduced accuracy when the timing of occurrence data does not align with the environmental data. One solution is to ensure a close temporal match between the environment and the observation date. While this approach is typically recommended for highly mobile species, a few findings support its use for species with limited mobility, whose distributions may be responding to climate change via local population changes. Additionally, it remains unclear what specific temporal resolution could improve model performance. This study assesses the effectiveness of temporal matching for a species with low mobility, the Mexican small-eared shrew (Cryptotis mexicanus), by evaluating different temporal resolutions (one-, five-, and ten-year averaged environmental data) against the standard method (30-year). Occurrences between 1971 and 2000 were used for model training and cross-validation, while those outside this range were used for external evaluation. Based on the omission rate of the external evaluation occurrences, the approach that matched environmental data using the prior ten-year resolution performed better than the standard 30-year average approach, while the rest of evaluation metrics (for any temporal resolution) were not different. Visual inspection indicated that the geographic prediction resulting from a ten-year resolution was as realistic as the one from the standard 30-year approach. In contrast, the shorter temporal resolutions (one and five years) resulted in unrealistic estimates. Therefore, matching the timing of occurrences and environmental data for other species with low mobility may also improve model performance and geographic predictions. Additionally, this correlative approach identifies a potential time lag between climatic changes and population responses in this species. Studies can select the optimal temporal resolution by exploring several or using available information about population responses to climate change.