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AbstractIn an era of rapidly changing climate, investigating the impacts of climate parameters on major air pollutants such as Particulate matter (PM10) is imperative to mitigate its adverse effect. This study utilizes Geographic Information System (GIS), a multivariate regression model (MVR) and Pearson correlation analysis to examine the inter-relationship between PM10and major climate parameters such as temperature, wind speed, and humidity. Although the application of MVR for predicting PM10 has been examined in previous studies, however, the spatial modelling and prediction of this air pollutant is limited. Accurate spatial assessment of pollutants’ hazard susceptibility in relation to climate change can accelerate mitigation initiatives. Thus, to understand the behavior, seasonal pattern, and trend of PM10concentration which is vital for good air quality, GIS is essential for enhanced visualization and interpretation of the predicted occurrence of the pollutant. The acquired data were randomly divided into 80% and 20% for training and validation of the MVR model, respectively while GIS was used to model the spatial distribution of the predicted ambient PM10 concentration, highlighting the hotspots of future PM10hazard. A positive correlation index was obtained between PM10with temperature and wind speed. However, humidity showed a negative correlation. The regression model showed high predictive performance of R2= 0.298, RMSE = 12.737, and MAE of 10.343, with the highest PM10concentration correlated with the warming event in the southwest monsoon. Temperature, wind speed, and humidity were identified as the most critical variables influencing PM10concentration in the study area, in descending order of importance. This study’s outcome provides valuable spatio-temporal information on future climate change impact on PM10in the study area with the potential to support effective air quality management.