Strong feedback interactions occur between terrestrial ecological processes and climate. However, vegetation dynamics have not been adequately reproduced by many large-scale ecosystem models and coupled climate models because local environmental variations are not sufficiently considered. By estimating subgrid-scale ecosystem productivity potentials within large grid cells, we explicitly incorporated local edaphic heterogeneity into the Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM). We assumed that high-resolution land cover data reflected the underlying ecosystem productivity potentials originating from edaphic variations. We categorized high-resolution Global Land Cover 2000 database (GLC2000) pixels into high, intermediate, and low productivity potentials and obtained fractional cover of these potentials on a DGVM grid. Then we performed transient dynamic vegetation simulations on circumpolar boreal regions with high, intermediate, and low photosynthetic capacities. Using the productivity potentials as weight, we estimated total leaf area index (LAI) that integrates the smaller-scale variation of each DGVM grid. Ground-based observations and a remote sensing product were used to evaluate the model performance. The regional pattern of simulated LAI was significantly improved, especially for the North American boreal regions. Improvement was minor for northern Eurasia, suggesting the qualitative classification by GLC2000 was the critical control on our approach. The results suggested that even a rather simplistic consideration of subgrid-scale heterogeneity could significantly improve large-scale simulations.