Dynamic global vegetation models (DGVMs) are commonly evaluated against satellite-derived products such as biomass or tree-cover. Yet, most studies provide error metrics and maps that reveal where models fail rather than why. Here, we develop a residual-analysis framework to diagnose systematic mismatches between aDGVM results and remotely sensed aboveground biomass and tree cover and attribute them to climate, fire, and human influence. We computed grid-cell residuals between aDGVM simulations and remote-sensing estimates and modeled these residuals using generalized additive models (GAMs) with bioclimatic variables, fire activity, human footprint and population density, as well as a spatial smooth. Models were checked for concurvity and basis-dimension adequacy, and evaluated using spatial block cross-validation. For aboveground biomass, a GAM with twelve smooth terms explained 80.3 % of residual deviance (adjusted <em>R²</em>= 0.798), while for tree cover, a GAM with similar structure explained 78.5 % of residual deviance (adjusted <em>R²</em>= 0.78). Strikingly, bioclimatic variables dominated residuals in 82.9 % of AGB grid cells, not the processes known to be absent from aDGVM, such as nitrogen cycling and land use. This suggests that re-calibrating existing climate responses would reduce model bias more than adding missing process modules. Fire and human pressure played a proportionally larger role for tree cover (42.3 % of grid cells) than for biomass (17.3 %), reflecting distinct ecological controls on net primary production versus canopy dynamics. A persistent spatial signal after accounting for all predictors (unique <em>R²</em> = 11.0 % AGB, 9.6 % TC) points to unmeasured processes including soil properties, wild megafauna, and land-use history. The framework transforms descriptive DGVM error maps into quantitative, spatially explicit process attribution directly translating model-data mismatches into prioritized development targets for next-generation vegetation models.