Biogenic emission models are developed on the foundation of leaf physiological processes and driven by a set of physical and biological factors. To estimate emissions online, many studies used weather forecasting models coupled with simple biogenic emission algorithms, in which the canopy physiological parameters were neglected or oversimplified. In this study, the land surface scheme CLM4 (Community Land Model version 4) coupled in the advanced Weather Research and Forecasting model (WRF) was used to determine canopy physiological parameters. The MEGAN (Model of Emissions of Gases and Aerosols from Nature) algorithms embedded in CLM4 scheme used these parameters to estimate biogenic emissions. The emission estimated by using leaf temperature in our study were about 23% higher than that based on air temperature as in the previous methods. Compared with studies neglecting shaded canopy, the separate treatments of sunlit and shaded leaves in this study lowered the estimations by a factor of 2 through decreasing diffuse radiaton absorbed by sunlit canopy. Dynamic weather history was used in our study to replace the fixed values in the original MEGAN-CLM4 code. An emission inventory of isoprene and monoterpenes in China was established for the year 2018. The estimates were evaluated against field measurements. Generally, the coupled model produced a reasonable simulation in both emission budgets and spatiotemporal distribution of biogenic emissions.