Terrestrial Gross Primary Productivity (GPP) is pivotal to the global carbon cycle, and its response to climate change is strongly regulated by topographic conditions through complex, non-linear mechanisms that remain poorly quantified at macro scales. Integrating multi-source remote sensing data with structural equation modeling (SEM), geographical detectors, and multivariate generalized additive models (GAM), this study investigated the spatiotemporal dynamics of GPP and its non-linear responses to coupled climate-topography gradients across China from 2001 to 2020. Results revealed that 79.3% of the vegetated area in China experienced an increasing GPP trend, of which 54.9% were statistically significant (p 3,000 m) regimes. Notably, mid-altitude regions (1,000–2,000 m) sustained strong long-term greening trends and a localized positive interannual sensitivity to precipitation, representing an ecological ‘sweet spot.’ Furthermore, by decoupling covarying environmental factors, we quantified critical ecological thresholds and their uncertainties for climatic drivers. We identified saturation points for temperature (~16.7 °C, with a wide 95% CI spanning from −4.9 to 18.9 °C), a more constrained saturation threshold for precipitation (~1,757 mm, 95% CI: 695–1,840 mm), and an inhibition threshold for solar radiation (>197 W m−2, 95% CI: 142–231 W m−2). These findings reveal robust statistical patterns of climatic constraints and non-linear thresholds in complex terrains. While our results are conditional on the GOSIF GPP product and the 2010 land-cover baseline, they provide a valuable empirical foundation for understanding topography-climate-vegetation interactions. These data- and model-conditional insights offer important implications for refining Earth system models and guiding region-specific carbon management strategies.