## 1 Introduction  
Gross primary production (GPP) refers to the total amount of organic carbon fixed by terrestrial ecosystems through photosynthesis by green plants [1]. It directly reflects the productivity of vegetation under specific environmental conditions and characterizes the growth characteristics and health status of terrestrial ecosystems [2]. As it is the largest and most important component of global carbon cycles, accurately evaluating the spatiotemporal dynamics of terrestrial GPP has significant implications for rational use of land resources, increase of carbon sinks, and adaptive strategy to climate change [3; 4; 5; 6]. The common methods for estimating GPP mainly encompass the continuous observations using the eddy covariance-based flux towers and satellite-based estimates with ecosystem models [7]. However, in situ measurements are usually constrained by scale and cost, leading to a spatially sparse distribution until now. Satellite remote sensing can obtain large-scale land surface data reflecting the vegetation-related and environmental variables quickly and cheaply [8]. In order to simulate the productivity over regional and global scales, several GPP models using the remote sensing data have been developed in past decades [9; 10; 11; 12; 13]. Subsequently, by means of these algorithms, the global GPP products were produced over a long period. Nevertheless, different products have large biases in performance due to the structures of models and the uncertainties in various input parameters. The moderate resolution imaging spectroradiometer (MODIS) GPP data have been produced continuously across the globe since 2000 and also experienced ongoing improvements in recent years [14; 15; 16; 17]. Currently, many studies on GPP in China are conducted with the earlier MODIS collection 5 (C5) vegetation products [18; 19]. However, the improved MODIS collection 6 (C6) GPP has been openly shared since the end of 2015, with the biome property look up tables (BPLUT) updated and the meteorological data of daily Global Modeling and Assimilation Office (GMAO) [20; 21]. More importantly, MODIS C6 contains significant revisions of the calibration approach to account for sensor aging [22]. Hence, it is urgent to examine the performance of the newly-released C6 products with 500 m resolution. Since the algorithm of MODIS GPP products was mainly restricted by the uncertainties from various upstream inputs, the products have always been controversial [23; 24; 25]. Aiming at the complexity of MODIS algorithms, [PERSON] and [PERSON] provided an alternative set of global GPP products using a simplified process-based model, the breathing Earth system simulator (BESS), for 8-day composite 1-km resolution [26]. The model is coupled with canopy photosynthesis, atmosphere and canopy radiative transfers, energy balance, and transpiration [27]. BESS GPP products were deemed reliable when compared against the MODIS C5.5 products, the coarse spatial resolution (0.5\({}^{\circ}\)) products from Max Planck Institute of Biochemistry, and the FLUXNET datasets at site scale (113 sites in total). Thus, the process-based BESS can serve as an independent set to complement the official MODIS GPP products. In addition, [PERSON] et al. [25] developed a global moderate resolution dataset of vegetation GPP for 2000-2016 using the vegetation photosynthesis model (VPM), which was based on the improved light use efficiency (LUE) theory [28; 29]. This product showed satisfactory performance across the wide range of biome types with regards to site-level validations [30; 31; 32]. Actually, all these GPP datasets have experienced amounts of validation using the FLUXNET sites in recent years. However, intercomparison among these new global GPP products particularly between BESS and VPM over various climate zones remained scarce. As the largest developing country in the world, China has emitted a lot of CO\({}_{2}\) accompanying its fast economic development since the reform and opening up in the 1980s [33; 34; 35].