Global climate change and intensifying human activities have substantially altered grassland productivity across China, which hosts one of the world's largest and most diverse grassland systems. By integrating multi-source remote sensing, biogeochemical modeling, machine learning, and spatial statistical analyses, we quantified spatiotemporal dynamics of actual net primary productivity (ANPP) across eight major grassland types in China from 2000 to 2019. Results reveal a widespread grassland restoration trend, with mean ANPP increasing by 3.34 g C center dot m- 2 center dot year- 1 and 68.4 % of grassland areas exhibiting restoration. Climate change emerged as the dominant driver, promoting productivity across 29.4 % of grasslands, primarily through enhanced precipitation. Human activities independently contributed to restoration across 14.1 % of areas but reduced productivity in many arid regions relative to climate-determined potential. Notably, synergistic interactions between climate change and human activities jointly promoted restoration across 24.9 % of grassland areas. Optimal parameter geographic detector analysis identified precipitation as the strongest control on ANPP spatial variability (q approximate to 0.62-0.73), while individual human activity factors showed weak independent effects. However, climate-human interactions consistently exhibited nonlinear enhancement effects. Future projections based on Random Forest models indicate that the SSP245 moderate-emission scenario yields the highest and most spatially stable grassland productivity by 2060.