###### Abstract  
The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie-Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC\(\cdot\)m\({}^{-2}\).yr\({}^{-1}\), and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai-Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000-5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage. large scale; net ecosystem production; spatiotemporal analysis; high-elevation grasslands 132020202018 March 2021 carbon stock and budget of temperate grasslands [1] and alpine grasslands [10; 11]. There is still great uncertainty regarding the carbon sources and sinks of grassland ecosystems because grassland may sometimes behave as carbon sources. Most of the fluctuations in these carbon sources and sinks are caused by dramatic changes in climate [2]. For example, in the event of drought or extreme wetness, grasslands may release carbon dioxide into the atmosphere [12; 13]. Climate fluctuations usually limit the growth of grasslands and are the main cause of interannual changes in carbon sinks because they greatly reduce NPP and NEP [14]. However, most studies have focused on small regional scales of eddy covariance flux observations and fixed-point experiments. Although there have been studies using machine learning to upscale based on point observation data in order to achieve large-scale inversion [15; 16; 17], the upscaling process is a function of the flux data available and can induce uncertainty, particularly in regions that have few flux sites [18]. Therefore, the NEP inversion model based on remote sensing data is the guarantee for realizing large-area research, and it is more conducive to evaluating macroscopic patterns. With the development of remote sensing technology, many vegetation indexes have been found to be closely related to the carbon cycle, and large-scale climate data have been generated, which provide new methods for estimating NEP over large areas. The Hindu Kush Himalayan (HKH) region is a massive alpine landform feature. Approximately half of the HKH region is composed of grassland, which serves as a resource for supporting 240 million mountain people who are dependent on nomadic livestock grazing [19; 20]. In the context of global climate change, grassland ecosystems in the HKH region are relatively fragile and are also highly sensitive to various climate disturbances. In recent years, due to overgrazing and extreme weather events, grassland ecosystems in the HKH region have undergone extensive degradation, causing continuous fluctuations in carbon sources and sinks. Recent studies have shown that changes in grassland dynamics and productivity have occurred in watershed or component areas in the HKH region [20; 21; 22].