Continental-scale maps of plant functional diversity are a fundamental piece of data of interest to ecosystem modelers and ecologists, yet such maps have been exceedingly hard to generate. The large effort to compile global plant functional trait databases largely for the purpose of mapping and analyzing the spatial distribution of function has resulted in very sparse data matrices thereby limiting progress. Identifying robust methodologies to gap fill or impute trait values in these databases is an important objective. Here I argue that existing statistical tools from phylogenetic comparative methods can be used to rapidly impute values into global plant functional trait databases due to the large amount of phylogenetic signal often in trait data. In particular, statistical models of phylogenetic signal in traits can be generated from existing data and used to predict missing values of closely related species often with a high degree of accuracy thereby facilitating the continental-scale mapping of plant function. Despite the promise of this approach, I also discuss potential pitfalls and future challenges that will need to be addressed.