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 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 a significant increasing trend in GPP across nearly 80% of China, with precipitation identified as the dominant driver, surpassing temperature and radiation. Topography significantly modulated climate sensitivity by redistributing hydrothermal resources. A distinct transition in dominant limiting factors was observed along the altitudinal gradient, shifting from water-limited (<2000 m) to energy-limited (>3000 m) regimes. Notably, mid-altitude regions (1000–2000 m) exhibited the highest sensitivity to precipitation, representing an ecological "sweet spot". Furthermore, we quantified critical ecological thresholds for climatic drivers, identified saturation points for temperature (~17.4°C) and precipitation (~1974 mm), and an inhibition threshold for solar radiation (>101 W/m²). These findings elucidate the transition mechanisms of climatic constraints and non-linear thresholds in complex terrain, providing robust scientific evidence for region-specific ecosystem and carbon management.