Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes.