Quantifying the dynamics of above and belowground biomass carbon (AGBC and BGBC) is essential for optimizing carbon sink management. However, the environmental thresholds that govern these dynamics under climate change remain poorly understood in China. In this study, we identified key thresholds by examining the relationships between AGBC (4485 observations) and BGBC (3442 observations) with mean annual temperature (MAT), aridity index (AI), and soil pH. Thresholds for AGBC were 15.24 degrees C (MAT), 1.17 (AI), and 6.87 (pH), while those for BGBC were 14.37 degrees C, 0.65, and 7.99, respectively. Additionally, we explored these thresholds in different ecosystems (forests, grasslands, shrublands, and wetlands). By spatially mapping these thresholds, we delineated environmentally sensitive areas-regions currently below (or above) the thresholds that are projected to exceed (or fall below) them under future climate scenarios. Using machine learning algorithms, we modeled AGBC and BGBC distributions for the years 2010 and 2100 (SSP5-8.5 scenario) and identified regions with the most significant expected changes. Overlaying threshold-sensitive areas with projected vegetation carbon changes revealed that AGBC is likely to increase in the southeastern Tibetan Plateau, while BGBC is projected to increase in the northern Shandong Province (Likelihood > 66 %). These shifts are primarily driven by regional warming and humidification that exceed identified MAT and AI thresholds. By integrating threshold identification with spatial and temporal analyses, this study enhances our understanding of vegetation carbon responses to climate change.