Significance: Drastic air pollution control in China since 2013 has achieved sharp decreases in fine particulate matter (PM _2.5 ), but ozone pollution has not improved. After removing the effect of meteorological variability, we find that surface ozone has increased in megacity clusters of China, notably Beijing and Shanghai. The increasing trend cannot be simply explained by changes in anthropogenic precursor [NO _x and volatile organic compound (VOC)] emissions, particularly in North China Plain (NCP). The most important cause of the increasing ozone in NCP appears to be the decrease in PM _2.5 , slowing down the sink of hydroperoxy radicals and thus speeding up ozone production. Decreasing ozone in the future will require a combination of NO _x and VOC emission controls to overcome the effect of decreasing PM _2.5 .

Surface ozone data from the Chinese Ministry of Ecology and Environment (MEE) network show sustained increases across the country over the 2013–2019 period. Despite Phase 2 of the Clean Air Action Plan targeting ozone pollution, ozone was higher in 2018–2019 than in previous years. The mean summer 2013–2019 trend in maximum 8 h average (MDA8) ozone was 1.9 ppb a⁻¹ (p<0.01) across China and 3.3 ppb a⁻¹ (p<0.01) over the North China Plain (NCP). Fitting ozone to meteorological variables with a multiple linear regression model shows that meteorology played a significant but not dominant role in the 2013–2019 ozone trend, contributing 0.70 ppb a⁻¹ (p<0.01) across China and 1.4 ppb a⁻¹ (p=0.02) over the NCP. Rising June–July temperatures over the NCP were the main meteorological driver, particularly in recent years (2017–2019), and were associated with increased foehn winds. NCP data for 2017–2019 show a 15 % decrease in fine particulate matter (PM_2.5) that may be driving the continued anthropogenic increase in ozone, as well as unmitigated emissions of volatile organic compounds (VOCs). VOC emission reductions, as targeted by Phase 2 of the Chinese Clean Air Action Plan, are needed to reverse the increase in ozone.

Fine particulate matter (PM_2.5) is a severe air pollution problem in China. Observations of PM_2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM_2.5 across China over the 2013–2018 period, averaging at −5.2 µg m⁻³ a⁻¹. Trends in the five megacity cluster regions targeted by the government for air quality control are $-\mathrm{9.3}\pm \mathrm{1.8}$ µg m⁻³ a⁻¹ (±95 % confidence interval) for Beijing–Tianjin–Hebei, $-\mathrm{6.1}\pm \mathrm{1.1}$ µg m⁻³ a⁻¹ for the Yangtze River Delta, $-\mathrm{2.7}\pm \mathrm{0.8}$ µg m⁻³ a⁻¹ for the Pearl River Delta, $-\mathrm{6.7}\pm \mathrm{1.3}$ µg m⁻³ a⁻¹ for the Sichuan Basin, and $-\mathrm{6.5}\pm \mathrm{2.5}$ µg m⁻³ a⁻¹ for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO₂) and carbon monoxide (CO) show that the declines in PM_2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM_2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM_2.5 trends across China. The MLR model correlates the 10 d PM_2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM_2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM_2.5 decrease across China is −4.6 µg m⁻³ a⁻¹ in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM_2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are $-\mathrm{8.0}\pm \mathrm{1.1}$ µg m⁻³ a⁻¹ for Beijing–Tianjin–Hebei (14 % weaker than in the original data), $-\mathrm{6.3}\pm \mathrm{0.9}$ µg m⁻³ a⁻¹ for the Yangtze River Delta (3 % stronger), $-\mathrm{2.2}\pm \mathrm{0.5}$ µg m⁻³ a⁻¹ for the Pearl River Delta (19 % weaker), $-\mathrm{4.9}\pm \mathrm{0.9}$ µg m⁻³ a⁻¹ for the Sichuan Basin (27 % weaker), and $-\mathrm{5.0}\pm \mathrm{1.9}$ µg m⁻³ a⁻¹ for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM_2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.