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.

Aerosol optical depth (AOD) has become a crucial metric for assessing global climate change. Although global and regional AOD trends have been studied extensively, it remains unclear what factors are driving the inter-decadal variations in regional AOD and how to quantify the relative contribution of each dominant factor. This study used a long-term (1980–2016) aerosol dataset from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellite-based AOD datasets (MODIS/Terra and MISR) from 2001 to 2016, to investigate the long-term trends in global and regional aerosol loading. Statistical models based on emission factors and meteorological parameters were developed to identify the main factors driving the inter-decadal changes of regional AOD and to quantify their contribution. Evaluation of the MERRA-2 AOD with the ground-based measurements of AERONET indicated significant spatial agreement on the global scale (r= 0.85, root-mean-square error = 0.12, mean fractional error = 38.7 %, fractional gross error = 9.86 % and index of agreement = 0.94). However, when AOD observations from the China Aerosol Remote Sensing Network (CARSNET) were employed for independent verification, the results showed that MERRA-2 AODs generally underestimated CARSNET AODs in China (relative mean bias = 0.72 and fractional gross error $=-\mathrm{34.3}$ %). In general, MERRA-2 was able to quantitatively reproduce the annual and seasonal AOD trends on both regional and global scales, as observed by MODIS/Terra, although some differences were found when compared to MISR. Over the 37-year period in this study, significant decreasing trends were observed over Europe and the eastern United States. In contrast, eastern China and southern Asia showed AOD increases, but the increasing trend of the former reversed sharply in the most recent decade. The statistical analyses suggested that the meteorological parameters explained a larger proportion of the AOD variability (20.4 %–72.8 %) over almost all regions of interest (ROIs) during 1980–2014 when compared with emission factors (0 %–56 %). Further analysis also showed that SO₂ was the dominant emission factor, explaining 12.7 %–32.6 % of the variation in AOD over anthropogenic-aerosol-dominant regions, while black carbon or organic carbon was the leading factor over the biomass-burning-dominant (BBD) regions, contributing 24.0 %–27.7 % of the variation. Additionally, wind speed was found to be the leading meteorological parameter, explaining 11.8 %–30.3 % of the variance over the mineral-dust-dominant regions, while ambient humidity (including soil moisture and relative humidity) was the top meteorological parameter over the BBD regions, accounting for 11.7 %–35.5 % of the variation. The results of this study indicate that the variation in meteorological parameters is a key factor in determining the inter-decadal change in regional AOD.