Accurately estimating the spatiotemporal dynamics of global gross primary productivity (GPP) and its underlying mechanisms is crucial for understanding the global carbon cycle and climate change. Satellite remote sensing enables continuous observation of large-scale vegetation dynamics, providing valuable opportunities to study the spatial and temporal variations of GPP on a global scale. However, different GPP products often exhibit significant discrepancies in global GPP estimates, and a comprehensive validation and comparison of these products at the global level has not yet been conducted. Therefore, the spatiotemporal consistency and interannual trends of eight GPP products (EC-LUE, GLASS, GOSIF, MOD17A2H, MuSyQ, PMLv2, EC-LUE, and VPM) during 2003—2014 were evaluated by using observations from 147 global flux towers. Spatiotemporal analysis revealed exceptionally strong temporal correlations among products (R2>0.960). Spatially, all products exhibited high comparability (R2≥0.702) except GOSIF, which showed weaker consistency with others (R2≤0.573). Annual GPP estimates ranged from 678.3g/(m2·a) (MOD17A2H) to 1223.0g/(m2·a) (GOSIF). All products except GLASS displayed increasing trends, with the northern hemisphere dominating the GPP increase. The proportion of ascending areas varied substantially across products, peaking in VPM (72.4%) and reaching a minimum in GOSIF (45.2%). Validation against flux tower data identified PMLv2 as the best-performing product (R2=0.664), while EC-LUE (R2=0.547) and GLASS (R2=0.572) showed relatively lower accuracy. Systematic overestimations were observed in GOSIF, GLASS, and PMLv2 across most sites. Furthermore, the products demonstrated higher accuracy in America, high-latitude regions, and wetlands (WET) and evergreen needleleaf forests (ENF). The research results were significant for improving the ability of ecosystem process models to simulate different regions, and they contributed to a deep understanding of regional ecosystem carbon dynamics and the global carbon cycle.