Atmospheric fine particulate matter (PM(2.5)) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM(2.5)-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM(2.5) interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM(2.5) IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM(2.5) IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM(2.5) concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM(2.5) IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM(2.5) IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM(2.5) IAV than emission variations. Thus, when setting policies for complying with the WHO PM(2.5)-related air quality guideline levels, the highest annual PM(2.5) associated with climate extremes should be considered instead of that based on average climate conditions.