## 1 Introduction  
Climate change and climate extremes always exert a substantial impact on the biosphere, but the response, as well as the underlying mechanism, vary among different vegetation types [1]. Especially under the context of global warming, the arid and semi-arid (ASA) regions are projected to expand further in the coming decades [2; 3]. [PERSON] [4] found that the dryland had enlarged by 4-8%over the past century, and covered almost 47% of the Earth's surface, among which the ASA ecosystems accounted for a large part (about 64%) and were indispensable parts of the global carbon balance. Furthermore, drylands are vulnerable to environmental changes, resulting in land degradation called desertification [5]. [PERSON] and [PERSON] [6] found that around 2% of the global gross primary productivity (GPP) was lost each year due to climate and human-induced dryland degradation. Research has found that more than half of the exceptionally large land carbon sink was caused by semi-arid vegetation in Australia [7]. [PERSON] [8] also found that the carbon budgets at diverse vegetation types differed widely. Although many methods have been developed, for example, remote sensing inversion [9] and process-based simulation [8; 10], it remains difficult to accurately quantify the carbon fluxes across ASA ecosystems. Recently, the eddy covariance (EC) technique has been of increasing importance to get the net ecosystem carbon exchange (NEE) between the ecosystem and the atmosphere because of its directness and continuity [11; 12]. As a result, evaluations of the differences in carbon sequestration among diverse ecosystems on the Earth as well as the underlying mechanisms are emerging [13; 14]. Meanwhile, the value of GPP can be indirectly calculated based on the continuous observation recorded [15]. However, it is difficult to directly upscale the site-level observations to a large region. Semi-empirical approaches [16] and process-based models [17] play significant roles in generating space-time continuous GPP datasets. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP used the light use efficiency (LUE) models, based on [PERSON]'s production efficiency concept [18]. As the earliest and most commonly used GPP product, the MODIS GPP has been updated to version 6 with a finer spatial resolution of 500 m. The Breathing Earth System Simulator (BESS) is a succinct process-based model for continuously monitoring carbon and water fluxes [17]. BESS-derived GPP coupled photosynthesis and transpiration [19], atmospheric and land processes [20]. Furthermore, it used a two-leaf model [21] and considered foliar clumping effects [22]. Global BESS GPP datasets were generated by integrating remote sensing data with process-based models. In recent years, two global GPP products from the MODIS and BESS have become widely used. Compared with the MODIS GPP, the BESS GPP allows a thorough understanding of the potential interaction mechanisms and synergetic responses to environmental change [23]. In addition, satellite-based measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new opportunity for studying the GPP at regional to global scales. Based on the global SIF product measured by the Orbiting Carbon Observatory-2 (OCO-2), [PERSON] and [PERSON] [24] mapped the GOSIF GPP globally at a 0.05\({}^{\circ}\) spatial resolution and eight-day time step for the period from 2000 to 2018. The accuracy and reliability of these products are the keys to large-scale applications. To meet the actual requirements, the performance of these products needs to be evaluated in advance across diverse ecosystems [25; 26]. Based on MODIS GPP datasets, [PERSON] [27] analyzed the space-time distribution of carbon sinks at the three-river headwaters region, but only focused on a single vegetation ecosystem.