Abstract Energy data are often reported on an annual basis. To address the climate and health impacts of greenhouse gases and air pollutants, seasonally resolved emissions inventories are needed. The seasonality of energy consumption is most affected by consumption in the residential sector. In this study, a set of regression models were developed based on temperature-related variables and a series of socioeconomic parameters to quantify global electricity and fuel consumption for the residential sector. The models were evaluated against observations and applied to simulate monthly changes in residential energy consumption and the resultant emissions of air pollutants. Changes in energy consumption are strongly affected by economic prosperity and population growth. Climate change, electricity prices, and urbanization also affect energy use. Climate warming will cause a net increase in electricity consumption and a decrease in fuel consumption by the residential sector. Consequently, emissions of CO2, SO2, and Hg are predicted to decrease, while emissions of incomplete combustion products are expected to increase. These changes vary regionally.