[Objective] The temporal and spatial variation of net primary productivity (NPP) of vegetation in Tibet and its relationship with influencing factors were determined in order to provide a scientific basis for green ecological construction and the development of national land resources in the region. [Methods] We used MOD17A3 NPP data, meteorological data, and spatial distribution data of vegetation types with the methods of trend analysis, bias analysis, correlation analysis, and geodetector to analyze the spatial-temporal patterns and influencing factors of NPP in Tibet from 2000 to 2020. [Results] ① NPP in most areas of Tibet showed an increasing trend from 2000 to 2020. The average annual NPP was 137.36 g/(m²·a), ranging from 128.04 g/(m²·a) to 148.06 g/(m²·a). The overall distribution of NPP exhibited obvious spatial heterogeneity, with high values in the southeast and low values in the northwest. ② There were significant differences in annual NPP among different vegetation types, with broadleaf forest having the highest NPP of 1 107.09 g/(m²·a) and desert having the lowest NPP of 29.76 g/(m²·a). The mean values followed the order of broadleaf forest ＞ cultivated vegetation ＞ coniferous forest ＞ shrub ＞ meadow ＞ alpine vegetation ＞ grassland ＞ desert. ③ NPP was significantly correlated with both temperature and precipitation. NPP was mostly negatively correlated with precipitation and positively correlated with temperature. ④ The explanatory power of each influencing factor on the spatial variability of NPP followed the order of rainfall ＞ altitude ＞ temperature ＞ slope ＞ vegetation type ＞ land use type. Natural factors had a stronger influence on NPP than anthropogenic factors. [Conclusion] The interaction of double factor was stronger than that of single factor. The distribution of water and heat conditions in Tibet was uneven, and the spatial distribution of NPP varied greatly. The overall condition of ecosystems in Tibet has generally improved over time, but local ecosystems were found to be relatively fragile.