Carbon sequestration occurs only when terrestrial ecosystems are at nonsteady states. Despite of their ubiquity in the real world, the nonsteady states of ecosystems have not been well quantified, especially at regional and global scales. In this study, we developed a two-step data assimilation scheme to estimate carbon sink strength in China's forest ecosystems. Specifically, the two-step scheme consists of a steady state step and a nonsteady state step. In the steady state step, we constrained a process-based model (Terrestrial Ecosystem Regional (TECO-R) model) against biometric data (net primary production NPP, biomass, litter, and soil organic carbon) in mature forests. With a subset of the parameter values estimated from the steady state data assimilation being fixed, the nonsteady state data assimilation was performed to estimate carbon sequestration in China's forest ecosystems. Our results indicated that 17 out of the 22 total parameters in the TECO-R model were well constrained by the biometric data with the steady state data assimilation. When observations from both mature and developing forests were used, all the 10 parameters related to carbon sequestration in vegetation and soil carbon pools were well constrained at the nonsteady state step. The estimated mean vegetation carbon sink in China's forests is 89.7 ± 16.8 gC m−2 yr−1, comparable with the values estimated from the forest inventory and other process-based regional models. The estimated mean soil and litter carbon sinks in China's forests are 14.1 ± 20.7 and 4.7 ± 6.5 gC m−2 yr−1. This study demonstrated that a two-step data assimilation scheme can be a potent tool to estimate regional carbon sequestration in nonsteady state ecosystems.