Aim: Bioclimatic envelope models are widely used to describe changes in climatically suitable areas for species under future climate scenarios. Climate change metrics are applied independently of species data to characterize the spatio-temporal dynamics of climate, and have also been used as indicators of the exposure of species to climate change. Here, we investigate whether these two approaches provide qualitatively similar indications about where biodiversity is potentially most exposed to climate change. Location: Sub-Saharan Africa. Methods: We compared a range of climate change metrics for sub-Saharan Africa with ensembles of bioclimatic envelope models for 2723 species of amphibians, snakes, mammals and birds. For each taxonomic group, we performed three comparisons between the two approaches: (1) is projected change in local climatic suitability (models) greater in grid cells with larger temporal differences in local climate (metrics); (2) are projected losses or gains of climatically suitable areas (models) greater for species in grid cells with climates that are projected to be less or more available in the future, respectively (metrics); and (3) are projected shifts in the position of climatically suitable areas (models) greater for species in grid cells with climates projected to move farther in space (metrics)? Results: The changes in climatic suitability projected by the bioclimatic envelope models covaried with the climatic changes measured with the metrics. Agreement between the two approaches was found for all taxonomic groups, although it was stronger for species with a narrower climatic envelope breadth. Main conclusions: For sub-Saharan African vertebrates, projected patterns of exposure to climate change given by climate change metrics alone were qualitatively comparable to bioclimatic model projections of changes in areas of suitable climate for species. Assessments based on climate change metrics can thus be useful for making first-cut inferences about the potential effects of climate change on regions with poorly known biodiversity.