Accurate electricity demand forecasts that account for impacts of extreme weather events are needed to inform electric grid operation and utility resource planning, as well as to enhance energy security and grid resilience. Three common data-driven models are used to predict city-scale daily electricity usage: linear regression models, machine learning models for time series data, and machine learning models for tabular data. In this study, we developed and compared seven data-driven models: (1) five-parameter change-point model, (2) Heating/Cooling Degree Hour model, (3) time series decomposed model implemented by Facebook Prophet, (4) Gradient Boosting Machine implemented by Microsoft lightGBM, and (5) three widely-used machine learning models (Random Forest, Support Vector Machine, Neural Network). Seven models are applied to the city-scale electricity usage data for three metropolitan areas in the United States: Sacramento, Los Angeles, and New York. Results show seven models can predict the metropolitan area’s daily electricity use, with a coefficient of variation of the root mean square error (CVRMSE) less than 10%. The lightGBM provides the most accurate results, with CVRMSE on the test dataset of 6.5% for Los Angeles, 4.6% for Sacramento, and 4.1% for the New York metropolitan area. These models are further applied to explore how extreme weather events (e.g., heat waves) and unexpected public health events (e.g., COVID-19 pandemic) influence each city’s electricity demand. Results show weather-sensitive component accounts for 30%–50% of the total daily electricity usage. Every degree Celsius ambient temperature increase in summer leads to about 5% (4.7% in Los Angeles, 6.2% in Sacramento, and 5.1% in New York) more daily electricity usage compared with the base load in the three metropolitan areas. The COVID-19 pandemic reduced city-scale electricity demand: compared with the pre-pandemic same months in 2019, daily electricity usage during the 2020 pandemic decreased by 10% in April and started to rebound in summer.