This paper provides an evaluation of the reliability of several climate models with respect to ecosystem modeling and describes a methodology for undertaking such an evaluation. The global climate models (GCM) and global vegetation models used in this study are linked in a one way mode (i.e., no feedback from the vegetation model to the GCM is allowed), and the vegetation distributions are compared with those obtained using observed climatology. The aim of this study is to identify which simulated ecosystems are sensitive to the biases of the climate simulations. Two global static vegetation models, BIOME-1 and a version of the Holdridge scheme, are used in conjunction with several present-day climate simulations. The climate simulations employed come from the GCMs participating in the Model Evaluation Consortium for Climate Assessment project. The results indicate that the overall performance of coarse resolution GCMs with respect to vegetation prediction is poor. The discrepancies between vegetation distributions computed from observed and simulated climatologies represent more than 50% of land area. The comparison of vegetation distributions shows that there are some common tendencies amongst these GCMs to induce the overprediction or underprediction of certain biomes. For example, the biomes belonging to dry climate regions are underpredicted, and the woodlands and temperate/cold forests are overpredicted. The climatic variables responsible for the discrepancies between vegetation predictions are identified, and it appears that the differences in vegetation predictions are overall due to the overestimation of the soil moisture index and precipitation, to the overestimation of growing degree days, and to the underestimation of the annual minimum temperatures. In summary, this research has shown that the prediction of biomes using simulated climatologies is not yet fully satisfactory; however, it is possible to increase our level of confidence in the prediction of vegetation by carefully evaluating the performance of the vegetation models driven by simulated climatologies and by identifying the causes of the biases.