China’s New EPICC Air Quality Model Rivals Global Giants in Landmark Head-to-Head Test

China’s air quality story over the past decade has been one of dramatic transformation. Aggressive emission controls cut population-weighted exposure to fine particulate matter, or PM2.5, by roughly 48 percent between 2013 and 2020, yet severe winter haze still blankets the North China Plain each year, and warm-season ozone has been climbing at a rate of about 1.2 parts per billion annually. Understanding and predicting this dual challenge of particulate and ozone pollution falls to chemical transport models, the numerical workhorses that translate emissions and meteorology into forecasts of what people actually breathe. Now, a team of Chinese researchers has put the country’s newest entrant in this arena, the Emission and atmospheric Processes Integrated and Coupled Community Model, better known as the EPICC-Model, through its most rigorous test yet: a nationwide, year-long, three-way comparison against two of the world’s most widely used air quality models, CAMx and CMAQ.

The EPICC-Model, developed with support from the National Natural Science Foundation of China and the Earth System Numerical Simulation Facility, was officially released in November 2024. It is a three-dimensional tropospheric chemical transport model with a modular, plug-in architecture that simulates emissions, transport, gas-phase and heterogeneous reactions, aerosol thermodynamics, and dry and wet deposition. Several of its process routines and numerical algorithms were inherited or adapted from CAMx and CMAQ, whose foundational contributions the developers explicitly acknowledge, but the EPICC team independently integrated specialized chemistry treatments, including sulfate formation, heterogeneous nitrous acid, or HONO, production, and the hydrolysis of dinitrogen pentoxide. The new study, published in Geoscientific Model Development, represents the first comprehensive benchmarking of the model against established international systems under controlled conditions.

The experimental design is what gives the comparison its power. All three models were driven by identical meteorological fields generated with the Weather Research and Forecasting model and a harmonized multi-source emission inventory built around Tsinghua University’s MEIC inventory for China, supplemented by PKU-NH3 ammonia emissions, a biomass burning inventory, and EDGAR data for regions outside China. Biogenic emissions came from the MEGAN model run online. The simulation covered all of 2021 on a nested grid with 15-kilometer resolution over China, and the results were evaluated against hourly PM2.5 and ozone observations from 1,644 national monitoring stations across 337 cities. The WRF meteorology itself passed stringent validation, with temperature correlations above 0.7 in most regions and humidity errors within acceptable bounds, ensuring that differences among the air quality models reflected their internal chemistry and physics rather than divergent weather inputs.

For PM2.5, the verdict was nuanced. CAMx delivered the best overall statistics, with 94 percent of predictions falling within a factor of two of observations and a normalized mean bias of just minus 2.86 percent, though it consistently missed severe pollution episodes. CMAQ achieved the highest correlation, 0.85, but systematically overestimated concentrations, a bias the authors traced largely to inflated organic carbon levels linked to its Potential Combustion Secondary Organic Aerosol parameterization. The EPICC-Model landed in between, showing a pronounced seasonal response: it tracked CAMx closely during the low-concentration spring and summer months, then shifted toward CMAQ-like behavior in autumn and winter, capturing pollution accumulation more effectively. Spatially, the EPICC-Model achieved the highest annual index of agreement, 0.80, compared with 0.78 for CAMx and 0.77 for CMAQ, and it performed especially well in winter and over heavily polluted North China, where its index of agreement reached 0.81.

Ozone told a different story, and it is here that the new model made its most striking impression. All three models reproduced the seasonal cycle of the daily maximum 8-hour average ozone concentration, but during summer peaks, when photochemistry runs at full throttle, both CAMx and CMAQ systematically underestimated the highest values by as much as 20 micrograms per cubic meter. The EPICC-Model captured these peaks with markedly smaller bias and achieved the lowest annual normalized mean bias of the three. The authors attribute this advantage, at least in part, to the model’s inclusion of heterogeneous HONO formation and particulate nitrate photolysis, chemical pathways that supply additional hydroxyl radicals and accelerate ozone production over inland China. Recent sensitivity studies with the EPICC-Model support this interpretation, showing that these radical sources measurably improve simulated ozone formation across the country.

The model’s advantage extended into operational territory. When the researchers scored categorical forecasts of pollution events, the EPICC-Model posted the highest hit rate, 45.6 percent, for ozone pollution episodes exceeding 160 micrograms per cubic meter, ahead of CAMx at 43.0 percent and CMAQ at 38.4 percent, while maintaining a low false alarm rate of 3.8 percent. For general PM2.5 pollution above 75 micrograms per cubic meter, CMAQ led with a 64.7 percent hit rate but paid for it with the highest false alarm rate, 9.6 percent, a signature of its chronic overprediction. CAMx was the most conservative forecaster, with the fewest false alarms but also the most misses. The EPICC-Model struck a balance between detection and false alarms, and it showed competitive accuracy in the light and moderate pollution categories that matter most for day-to-day public health advisories.

The study also probed how well the models handle the hardest test in the business: persistent, multi-day pollution episodes that spread across entire regions. In a January 2021 PM2.5 event that formed a dual-core pattern over the North China Plain and the Sichuan Basin before sweeping southeast toward the Yangtze, all three models captured the arc from buildup to cold-front clearance, but the EPICC-Model best reproduced the spatial gradients and temporal evolution, particularly over the North China Plain and the middle Yangtze corridor. CAMx underestimated the intensity and missed the strong core over Henan at the peak, while CMAQ overestimated throughout. A June ozone episode that stretched from the North China Plain to Guangzhou revealed a similar pattern, with the EPICC-Model capturing the broad regional extent of elevated ozone, though it smoothed the enhancement and locally overestimated concentrations in central and southeastern China during the peak stage.

Beneath the headline scores, the comparison exposed chemical fault lines that run through all three models. Observations show nitrate now dominates PM2.5 mass in most Chinese cities, accounting for roughly 31 to 42 percent, and all models reproduced this nitrate-dominated regime. Yet every model systematically underestimated sulfate, likely because of insufficient sulfur dioxide oxidation rates or gaps in aqueous-phase chemistry, a shortfall that cascades into reduced ammonium and, paradoxically, can push nitrate even higher as leftover ammonia partitions with nitric acid. Organic carbon remained the most uncertain component, with normalized bias stretching from minus 50 to plus 150 percent in some cities, reflecting deep uncertainties in secondary organic aerosol formation, precursor emissions, and gas-particle partitioning. Black carbon, the least abundant component, was simulated most consistently, though slight overestimations in Beijing and Zhengzhou hinted at emission allocation issues.

The authors are candid about the shared weaknesses. All three models failed to reproduce heavy pollution episodes well, with hit rates for severe PM2.5 events above 150 micrograms per cubic meter ranging from a dismal 5.4 to 26.8 percent, even as false alarm rates stayed below 2 percent. All underestimated PM2.5 in the arid northwest because none of the operational configurations included wind-blown dust, and all overestimated autumn and winter concentrations in North China and the Sichuan Basin, a bias the study links to boundary-layer schemes that under-mix the nocturnal atmosphere under stable conditions. Emission inventory uncertainties compounded the problem, with anthropogenic emissions likely overestimated in the Sichuan Basin and agricultural ammonia underestimated in Henan, distorting nitrate formation in both regions. The comparison also noted that boundary conditions were not harmonized across the models, meaning some differences reflect inflow assumptions rather than internal chemistry.

What emerges from this benchmark is a portrait of a young model that has already earned a seat at the table. The EPICC-Model matches or beats its established rivals where it matters most for Chinese policy: summer ozone peaks, winter haze patterns over the North China Plain, and the life cycle of persistent compound pollution events, advantages that appear tied to its distinctive heterogeneous chemistry. At the same time, the study charts a clear roadmap for improvement, calling for refined secondary aerosol chemistry, better treatment of vertical turbulent mixing under stable boundary layers, incorporation of dust processes, and higher-resolution emission inventories with improved temporal variability. Because the framework, code, and data are openly available, the comparison sets a template for how new atmospheric models anywhere can be rigorously vetted, and it signals that the tools for managing China’s air, and by extension the air of other rapidly developing regions, are becoming both more diverse and more capable.

Subject of Research: Comparative evaluation of the EPICC-Model chemical transport model against CAMx and CMAQ for simulating PM2.5 and ozone pollution across China

Article Title: Evaluating the EPICC-Model for regional air quality simulation: a comparative study with CAMx and CMAQ

Article References: Lou, M., Wu, Q., Wang, W., Chen, H., Cao, K., Fan, X., Liang, D., Yu, F., Zhang, J., Wang, W., & Wang, Z. (2026). Evaluating the EPICC-Model for regional air quality simulation: a comparative study with CAMx and CMAQ. Geoscientific Model Development, 19(19), 9651-9676. https://doi.org/10.5194/gmd-19-9651-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9651-2026

Keywords: EPICC-Model, air quality modeling, chemical transport models, PM2.5, ozone pollution, CMAQ, CAMx, China haze, secondary aerosols, HONO chemistry, model intercomparison, WRF meteorology