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The development of chemical transport models with advanced physics and chemical schemes could improve air-quality forecasts. In this study, the China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE) model, a comprehensive chemistry module incorporating gaseous chemistry and a size-segregated multicomponent aerosol algorithm, was coupled to the Weather Research and Forecasting (WRF) framework with chemistry (WRF-Chem) using an interface procedure to build the WRF/CUACE v1.0 model. The latest version of CUACE includes an updated aerosol dry deposition scheme and the introduction of heterogeneous chemical reactions on aerosol surfaces. We evaluated the WRF/CUACE v1.0 model by simulating PM 2.5 , O3 , NO2 , and SO2 concentrations for January, April, July, and October (representing winter, spring, summer and autumn, respectively) in 2013, 2015, and 2017 and comparing them with ground-based observations. Secondary inorganic aerosol simulations for the North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin (SCB) were also evaluated. The model captured well the variations of PM 2.5 , O3 , and NO2 concentrations in all seasons in eastern China. However, it is difficult to accurately reproduce the variations of air pollutants over SCB, due to its deep basin terrain. The simulations of SO2 were generally reasonable in the NCP and YRD with the bias at -15.5 % and 24.55 %, respectively, while they were poor in the Pearl River Delta (PRD) and SCB. The sulfate and nitrate simulations were substantially improved by introducing heterogeneous chemical reactions into the CUACE model (e.g., change in bias from -95.0 % to 4.1 % for sulfate and from 124.1 % to 96.0 % for nitrate in the NCP). Additionally, The WRF/CUACE v1.0 model was revealed with better performance in simulating chemical species relative to the coupled Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and CUACE model. The development of the WRF/CUACE v1.0 model represents an important step towards improving air-quality modeling and forecasts in China. [ABSTRACT FROM AUTHOR]