The Western United States (WUS) has experienced a significant increase in the frequency and intensity of fires, heatwaves, and droughts over recent decades, driven by climate change and other environmental factors. To better understand, monitor, plan for, and respond to these extremes, there is a critical need for comprehensive, high-resolution datasets that can provide detailed insights into their characteristics and patterns. In this study, we have compiled high-resolution datasets from multiple sources for fires, heatwaves, and droughts in the WUS, covering the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 1km × 1km. These datasets include co-located and relevant meteorological, surface, and vegetation-related variables. Preliminary analyses using these datasets reveal significant regional variations in the occurrence and severity of the extreme events, offering a better understanding of their characteristics and co-occurrences. The resulting data products can foster the use of AI and machine learning in advancing the understanding and prediction of extreme events, contributing to the development of improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of fires, heatwaves, droughts, and their compound events.