###### Abstract
This depends on their resistance and resilience to long-term climate change [24], leading to the induction of asynchronized inter-annual changes at seasonal or longer temporal resolutions [25]. Although the previously established positive relationship between greenness and GPP, recent studies have reported changes or even decoupled in the LAI-GPP relationship, especially over short timescales [22; 26; 27]. The correlation between greenness and productivity is influenced by numerous factors, including differences in observation timing and various environmental factors [10; 28]. A mismatch between greenness and productivity has been demonstrated in many studies. For instance, [PERSON] et al. (2011) found that greenness decreased in Amazonian rainforests during the 2010 drought, but vegetation productivity remained unchanged [29]. Conversely, [PERSON] et al. (2018) reported that during the 2015-2016 drought period, there was little change in greenness, while canopy photosynthesis decreased substantially [27]. Additionally, a weak correlation between vegetation greenness and GPP has also been reported in some ecosystems [30; 31]. [PERSON] et al. (2021) examined the spatiotemporal dynamics of greenness and productivity in South Asia and revealed that while there was a noticeable increase in greenness, the rise in productivity was comparatively modest [32]. Additionally, environmental factors also weaken the relationship between greenness and productivity. Studies validated that prolonged drought also causes a significant decline in greenness in arid ecosystems but has little effect on productivity [18]. Therefore, the response of GPP to changes in NDVI along aridity gradients needs to be fully investigated. The response of vegetation dynamics to climate change is a key process in external forcing, quantified by vegetation sensitivity, which indicates the degree and magnitude of vegetation anomalies and phenology in response to disturbances [33]. Recent studies have extensively investigated the sensitivity of vegetation to climate change (i.e., temperature, precipitation, drought, and CO\({}_{2}\)) [10; 34; 35; 36]. Generally, previous research has focused on specific regions or ecosystems over short time periods [37; 38], which provides valuable information on ecosystem resilience. High sensitivity might lead to slow recovery of vegetation from disturbances or even lead to ecosystem succession [33]. Nevertheless, the effects of long-term drying and wetting trends on vegetation productivity and greenness, particularly along the spatial aridity gradients across China, are still not well understood. Furthermore, these studies have often overlooked the analysis of the sensitivity trends and variations among different plant functional types (PFTs). Greenness and productivity are closely linked to aridity conditions, as water availability strongly restricts plant growth [39]. What is more, the alignment of inter-annual variations in productivity with greenness, and how this relationship changes spatially remain unclear. Despite these uncertainties, little attention has been paid to the impact of the aridity gradient during the growing season. Therefore, it is essential to thoroughly investigate how changes in NDVI and GPP respond to aridity. This will help clarify the trajectory and magnitude of vegetation activity across different ecosystems in response to temporal and spatial changes in the aridity index. Understanding these dynamics is crucial for grasping how plant growth responds to varying aridity conditions. Understanding the coupling between NDVI and GPP can provide insights into how changes in vegetation greenness correspond to variations in photosynthetic activity, while sensitivity analysis helps determine how responsive GPP is to changes in NDVI under different climatic conditions. Here, we used NDVI and GPP products to investigate the variability of the NDVI-GPP relationships across the aridity gradients in China during the growing season from 1982 to 2018.