Temperature sensitivity of soil respiration (Q(10)) is an important parameter in modeling the effects of global warming on ecosystem carbon release. Experimental studies of soil respiration have ubiquitously indicated that Q(10) has high spatial heterogeneity. However, most biogeochemical models still use a constant Q(10) in projecting future climate change and no spatial pattern of Q(10) values at large scales has been derived. In this study, we conducted an inverse modeling analysis to retrieve the spatial pattern of Q(10) in China at 8 km spatial resolution by assimilating data of soil organic carbon into a process-based terrestrial carbon model (CASA model). The results indicate that the optimized Q(10) values are spatially heterogeneous and consistent to the values derived from soil respiration observations. The mean Q(10) values of different soil types range from 1.09 to 2.38, with the highest value in volcanic soil, and the lowest value in cold brown calcic soil. The spatial pattern of Q (10) is related to environmental factors, especially precipitation and top soil organic carbon content. This study demonstrates that inverse modeling is a useful tool in deriving the spatial pattern of Q(10) at large scales, with which being incorporated into biogeochemical models, uncertainty in the projection of future carbon dynamics could be potentially reduced.