In a spatially explicit climate change impact assessment, a Bayesian network (BN) model was implemented to probabilistically simulate future response of the four major vegetation types in Swaziland. Two emission scenarios (A2 and B2) from an ensemble of three statistically downscaled coupled atmosphere-ocean global circulation models (CSIRO-Mk3, CCCma-CGCM3 and UKMO-HadCM3) were used to simulate possible changes in BN-based environmental envelopes of major vegetation communities. Both physiographic and climatic data were used as predictors representing the 2020s, 2050s and the 2080s periods. A comparison of simulated vegetation distribution and the expert vegetation map under baseline conditions showed an overall correspondence of 97.7% and a Kappa coefficient of 0.966. Although the ensemble projections showed comparable trends during the 2020s, the results from the A2 storyline were more drastic indicating that grassland and the Lebombo bushveld will be impacted negatively as early as the 2020s with about 1 °C temperature increase. The bioclimatically suitable areas of all but one vegetation type decline drastically after about 2 °C warming, more so under the more severe A2 scenario and in particular during the 2080s. The sour bushveld is the only vegetation type that initially responds positively to warming by possibly encroaching to the highly vulnerable grassland areas. Vulnerability of vegetation is increased by the limited ability to migrate into suitable climates due to close affinity to certain geological formations and the fragmentation of the landscape by agriculture and other land uses. This is expected to have serious impacts on biodiversity in the country. Under warmer climates, the likely vegetation types to emerge are uncertain due to future novel combinations of climate and bedrock lithology. The strengths and limitations of the BN approach are also discussed.