We present an approach to estimate gross primary production (GPP) using a remotely sensed biophysical vegetation product (fraction of absorbed photosynthetically active radiation, FAPAR) from the European Commission Joint Research Centre (JRC) in conjunction with GPP estimates from eddy covariance measurement towers in Europe. By analysing the relationship between the cumulative growing season FAPAR and annual GPP by vegetation type, we find that the former can be used to accurately predict the latter. The root mean square error of prediction is of the order of 250 gC m−2 yr−1. The cumulative growing season FAPAR integrates over a number of effects relevant for GPP such as the length of the growing season, the vegetation's response to environmental conditions and the amount of light harvested that is available for photosynthesis. We corroborate the proposed GPP estimate (noted FAPAR-based productivity assessment+land cover, FPA+LC) on the continental scale with results from the MOD17+radiation-use efficiency model, an artificial neural network up-scaling approach (ANN) and the Lund–Potsdam–Jena managed Land biosphere model (LPJmL). The closest agreement of the mean spatial GPP pattern among the four models is between FPA+LC and ANN (R2= 0.74). At least some of the discrepancy between FPA-LC and the other models result from biases of meteorological forcing fields for MOD17+, ANN and LPJmL. Our analysis further implies that meteorological information is to a large degree redundant for GPP estimation when using the JRC-FAPAR. A major advantage of the FPA+LC approach presented in this paper lies in its simplicity and that it requires no additional meteorological input driver data that commonly introduce substantial uncertainty. We find that results from different data-oriented models may be robust enough to evaluate process-oriented models regarding the mean spatial pattern of GPP, while there is too little consensus among the diagnostic models for such purpose regarding inter-annual variability.