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Multi-model ensembles (MMEs) have been demonstrated to be useful for improving the results of models. Furthermore, previous research suggests that weighted MMEs outperform unweighted multi-model results in climatological simulations, with the degree of difference in model performance dependent on the suitability of the weighting scheme. The goal of this research is to improve the results of MMEs by optimizing the weighting for each model for predicting the distribution of Koppen-Geiger climatic types. Results suggest that the correspondence between general circulation model- (GCM-) based and Climate Research Unit- (CRU-) based output of the geographic distribution of the Koppen-Geiger climatic types is inadequate for nine GCMs simulations, with only 40-52% of the total land area having agreement for the Koppen climatic type. An unweighted ensemble average GCM-based simulation produced only marginally improved model performance. However, the terrestrial area with disagreement between the MMEs and CRU was reduced to about 35% by using the nonlinear-weighted ensemble average. Inconsistent regions between the MMEs and CRU are concentrated along the climatic boundaries, confirming that ecotones are simulated poorly by these models. Because, the Koppen classification is designed so that climatic regions correspond to biomes, this research may have implications for improving simulation of agricultural, forestry, and other biotic realms under past and future climatic conditions.