Large urban centers like the Metropolitan Region of São Paulo (MASP) are impacted by air pollution, especially by Inhalable particle matter (PM10). Persistent exceedance events (PEE) are defined as exceedance events that last for many consecutive days and occur simultaneously at many air quality monitoring stations across the MASP. This study aims to develop a predictive model for the occurrence of PEE in the MASP based on surface meteorological variables. Hourly PM10 concentrations from 12 air quality monitoring stations in the MASP between 2005 and 2021 were provided by the São Paulo State Environmental Agency (CETESB). Daily data on surface meteorological variables were provided by the IAG/USP meteorological station. Persistent exceedance events (PEE) were identified using the criteria: exceedance events that occurred simultaneously in at least 50% monitoring stations, persisting for at least 5 consecutive days. PEE occurrence was represented as a timeseries of a binary variable. The resulting daily dataset had 6204 lines and 13 attributes, without missing values. The dataset was divided into a training set (80%) and a test set (20%). A logistic regression model was applied, having the PEE occurrence (positive = 1) as the target value. The Variance Inflation Factor and the Stepwise Feature Selection method was applied to obtain an optimized subset of predictors. Model accuracy was accessed by the ROC curve and by a confusion matrix. Results indicate that PEE can be satisfactorily predicted by surface meteorological variables using a logistic regression. As for the next steps, we intend to extract easy-tocommunicate classification rules, aiming to support the development of warnings systems for air quality poor conditions in the MASP.