Regional-scale air quality models are being used fo r studying the sources, composition, transport, 10 transformation, and deposition of fine particulate matter (PM2.5). The availability of decadal air quality simulati ons 11 provides a unique opportunity to explore sophistica ted model evaluation techniques rather than relying solely on 12 traditional operational evaluations. In this study, we propose a new approach for process-based model evaluation of 13 speciated PM2.5 using improved Complete Ensemble Empirical Mode De composition with Adaptive Noise (improved 14 CEEMDAN) to assess how well version 5.0.2 of the co upled Weather Research and Forecasting model Comm unity 15 Multiscale Air Quality model (WRF-CMAQ) simulates t he time-dependent long-term trend and cyclical vari ations in 16 the daily average PM 2.5 and its species, including sulfate (SO 4), nitrate (NO3), ammonium (NH4), chloride (Cl) organic 17 carbon (OC) and elemental carbon (EC) . The utility of the proposed approach for model evaluation is d emonstrated 18 using PM2.5 data at three monitoring locations. At these locati ons, the model is generally more capable of simulat ing 19 the rate of change in the long-term trend component than its absolute magnitude. Amplitudes of the sub easonal and 20 annual cycles of total PM 2.5, SO4 and OC are well reproduced. However, the time-depe ndent phase difference in the 21 annual cycles for total PM 2.5, OC and EC reveal a phase shift of up to half year , indicating the need for proper temporal 22 allocation of emissions and for updating the treatm ent of organic aerosols compared to the model versi on used for this 23 set of simulations. Evaluation of sub-seasonal and inter-annual variations indicates that CMAQ is more capable of 24 replicating the sub-seasonal cycles than inter-annu l variations in magnitude and phase. 25
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