Currently, the emission inventory of vehicular volatile organic compounds (VOCs) is one of those with the largest errors and uncertainties due to the imperfection of estimation methods and the lack of first-hand basic data. In this study, an updated speciated emission inventory of VOCs and an estimation of intermediate-volatility organic compounds (IVOCs) from vehicles in China at the provincial level, with a target year of 2015, were developed based on a set of state-of-the-art methods and a mass of local measurement data. The activity data for light-duty vehicles were derived from trajectories of more than 70 thousand cars for one year. The annual mileages of trucks were calculated from reported data by more than 2 million trucks in China. The emission profiles were updated using measurement data. Not only vehicular tailpipe emissions (VTEs) but also four kinds of vehicular evaporation emissions (VEEs), including refuelling, hot soak, diurnal and running loss, were taken into account. The results showed that the total vehicular VOCs emissions in China were 4.21 Tg and the IVOCs emissions were 121.23 Gg in the year of 2015. VTEs were still the predominant contributor, but VEEs were already responsible for 39.20 % of VOCs. Since VEES has a much less strict control standard, it should be paid much more attention to. Among VTEs, passenger vehicles contributed most (49.86 %), followed by trucks (28.15 %) and motorcycles (21.99 %). Among VEEs, running loss was the largest contributor (81.05 %). For both VTEs and VEEs, Guangdong, Shandong and Jiangsu province took the first three spots, with a respective contribution of 10.66 %, 8.85 % and 6.54 % to the total amounts of VOCs from vehicles. Totally, 97 VOC species were analysed in this VOCs emission inventory. I-pentane, toluene and formaldehyde were found to be the most abundant species in China's vehicular VOCs emissions. The estimated IVOCs is other <q>inconvenient truth</q>, providing insights the precursors of secondary organic aerosol (SOA) from vehicles were much more than previous estimation.