Variations in meteorology associated with climate change can impact fine particulate matter (PM 2.5 ) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model-based projections of PM 2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM 2.5 pollution in the United States. We find that natural variability in simulated PM 2.5 can be comparable or larger than reported estimates of anthropogenic-induced climate impacts. Relative to mean concentrations, the variability in projected PM 2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM 2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic-forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate-induced impacts on PM 2.5 . Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.Plain Language Summary Climate change can worsen air pollution caused by small particles in the atmosphere as it alters temperature, precipitation, and other weather variables. Models have been used to project the effects climate change can have on airborne particle concentrations. However, natural year-to-year and longer-term variations in weather can make it difficult to estimate the impacts specifically caused by human forces. In this study, we use a large set of climate and air quality simulations to assess the effect of natural variability in model-based projections of climate change impacts on air pollution over the United States. Our results show that natural variations in predictions of climate change effects on fine particle levels can be significant. We recommend that projections use longer simulation lengths than typically applied, 10 years or more, in order to filter out this natural variability and better reflect the effect of human-caused climate change on particle pollution. Natural variability can also make it challenging to confidently project human-caused climate impacts in some regions or if efforts are taken to reduce greenhouse gas emissions. By using larger modeling lengths or simulation sets, projections of climate change impacts on air pollution and climate policy benefit analyses can be impr...

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