The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol.