Abstract. The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. AnEn estimates the probability of future observations of a predictand based on a current deterministic numerical weather prediction and an archive of prior analog predictions paired with prior observations. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. The authors apply AnEn to observations from the Environmental Protection Agency's (EPA's) AIRNow network and to forecasts from the Community Multiscale Air Quality (CMAQ). Compared to raw forecasts from CMAQ, deterministic forecasts of O₃ and PM_2.5 based on AnEn's mean have lower errors, both systemic and random, and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.