Remote sensing (RS) and process coupled ecological models are widely used for simulating GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models did not differentiate the C3 and C4 photosynthetic pathways and neglected the effect of nitrogen content on Vcmax and Jmax leading to considerable bias in the estimation of the gross primary productivity (GPP). Here, we developed a model driven by the Leaf Area Index (LAI), climate and atmospheric CO2 concentration to estimate the global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 145 flux tower sites, which gave an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgC year-1 to 133.94 PgC year-1 from 2000 to 2022, with an average of 125.93 PgC year-1. We also found that the global GPP showed an increasing trend of 0.548 PgC PgC year-1 during the study period. Our further analyses using the structure equation model (SEM) showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes with the total association of 0.853 and 0.75, respectively, while precipitation had a minor but negative contribution to global GPP.