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Species distribution models (SDMs) link species occurrences to environmental descriptors using species and environmental data that are often recorded at different grain sizes. The upscaling process implied by grain size matching between species data and environmental data may affect the observed species distribution and thus might also modify the SDM-derived species distribution. Here we used five virtual species with differing range sizes to determine the effects of grain size on SDM-derived distribution area. We showed that the increase of SDM-derived distribution area with grain size is mainly due to the geometric increase of the area of the observed distribution range used to build the SDMs. Models based on presence–absence data that were built using the initial prevalence in the calibration dataset and the maximization of TSS or Kappa as cut-off threshold accurately predicted the observed area whatever the grain size and species range size. In addition we found that the commonly used evaluation measures (AUC, TSS and Kappa) cannot be used to evaluate the accuracy of SDM-derived distribution areas. Thus, the grain size of the data used to feed SDMs has to be chosen carefully, depending on the data quality and the goals of the study.