we apply a modified version of the national climatic data center asi . a grid cell day is considered stagnant when daily - mean near - surface ( 10-m ) wind speeds are < 3.2 m s , daily - mean mid - tropospheric ( 500 mb ) wind speeds are < 13 m s , and daily - mean precipitation accumulation is < 1 mm . the asi does not explicitly incorporate all meteorological factors known to influence air quality , including , relative humidity , temperature , turbulent mixing , and orographic barriers . consequently , asi component threshold sensitivities may vary locally / regionally and factors in addition to , or exclusive of , air stagnation may play a substantial role in determining local / regional air quality . global warming - driven stagnation changes are explored using an ensemble of realizations from 15 modeling groups that provide requisite daily three - dimensional atmospheric fields from both historical and rcp8.5 experiments of the climate model intercomparison project phase 5 ( table s2 ) . because the asi is reliant on absolute thresholds , systematic errors in cmip5-simulated stagnation - relevant variables are bias corrected using an empirical quantile mapping technique . to account for observational uncertainties , we use a suite of six unique reanalysis and observational dataset combinations ( see supplemental discussion ) . for wind correction , we use monthly ncep - doe r2 and ecmwf era - interim reanalysis 500 mb and 10-m components . for precipitation , we use monthly university of delaware v3.02 ( udel ) , global planetary climatology project v2.2 ( gpcp ) , and climate prediction center merged analysis of precipitation ( cmap ) data . bias correction of the 15 cmip5 models using six distinct combinations of observational standards forms what we refer to as the bias - corrected ensemble ( 90 members ) . for models with multiple realizations , we average all bias corrected realizations of each model prior to inclusion in the ensemble mean , such that each bias - corrected version of the model receives one vote ( see supplemental discussion ) . all model , reanalysis , and observational data are interpolated to 0.50.5. to determine stagnation differences , the mean annual occurrence in three future rcp8.5 periods ( 20162035 , 20462065 , & 20802099 ) is compared to the baseline historical period ( 19862005 ) . analysis of seasonal zonal wind speeds , meridional mass transport , and mid - tropospheric vertical velocity in figures 3 and s46 utilize monthly - scale cmip5 data and are not bias corrected . of the three asi components , the magnitude of dry day ( figure s8a , e , i ) and near - surface wind ( figure s8b , f , j ) condition biases are greatest , while mid - tropospheric ( 500 mb ) wind conditions show both the least bias and greatest improvement from correction ( figure s8c , g , k ) . baseline stagnation biases are greatest over high northern latitudes , northern australia , and amazonia , though our correction methodology substantially improves the mean occurrence ( figure s8d , h , l ) . extraction of a robust climate change signal from the bias - corrected ensemble follows a two - step process . first , statistical significance of the historic - to - future change in mean stagnation occurrence is assessed at each grid cell for each model using a non - parametric permutation test . for models with multiple realizations , statistical significance is assessed by comparing the combined population of historic realizations to that of the future . the permutation test makes no assumptions with regard to the data s underlying distribution , thereby yielding greater confidence in the resulting conclusions . following ipcc uncertainty guidance , change is considered statistically robust if at least 66% of ensemble members demonstrate change at or above the 95% confidence level . second , for those grid cells at which the simulated change is statistically robust , we assess ensemble member agreement in the direction of the simulated change . if fewer than 66% of bias - corrected ensemble members exhibit change at or above the 95% confidence level , we plot the grid cell as white . if at least 66% of members exhibit change at or above the 95% confidence level , but fewer than 66% agree on the direction of the statistically significant changes , we plot the grid cell as gray . if at least 66% of members exhibit change at or above the 95% confidence level , and at least 66% agree on the direction of statistically significant changes , we consider the asi - climate change signal robust , and plot ensemble - mean change in color ( figure 1 ) . for comparison , late - century ensemble - mean air stagnation change without robustness screening is included as figure s1 . stagnation changes presented in figures 1 and s1 are largely in agreement with cmip3-based results of horton et al ( 14 ) . differences are attributable to added robustness screening , and changes in ensemble composition , model structure , emission scenarios , and reanalysis / observational datasets . use of multi - model ensembles to capture model design uncertainties and create probabilistic projections of future climate outcomes has become standard practice , due in part to the organizing efforts of cmip . more recently , the air quality research community has adopted this protocol through independent efforts and under guidance from the atmospheric chemistry and climate model intercomparison project . in our analysis , bias correction , consideration of the uncertainties inherent in observational standards , application of statistical rigor , and establishment of multi - model agreement thresholds all provide additional confidence in the multi - model probabilistic projection of the likelihood of future stagnation changes .
poor air quality causes an estimated 2.6 to 4.4 million premature deaths per year13 . 
 hazardous conditions form when meteorological components allow the accumulation of pollutants in the near - surface atmosphere48 . 
 global warming - driven changes to atmospheric circulation and the hydrological cycle913 are expected to alter the meteorological components that control pollutant build - up and dispersal58,14 , but the magnitude , direction , geographic footprint , and public health impact of this alteration remain unclear7,8 . 
 we utilize an air stagnation index and an ensemble of bias - corrected climate model simulations to quantify the response of stagnation occurrence and persistence to global warming . 
 our analysis projects increases in stagnation occurrence that cover 55% of the current global population , with areas of increase affecting 10 times more people than areas of decrease . by the late-21st century , robust increases of up to 40 days per year are projected throughout the majority of the tropics and subtropics , as well as within isolated mid - latitude regions . 
 potential impacts over india , mexico , and the western u.s . 
 are particularly acute due to the intersection of large populations and increases in the persistence of stagnation events , including those of extreme duration . 
 these results indicate that anthropogenic climate change is likely to alter the level of pollutant management required to meet future air quality targets .