BACKGROUND: Climate change has increased the frequency and intensity of extremely hot weather. The health risks are not uniform across affected areas due to variability in heat exposure and social vulnerability, but these differences are challenging to map with precision. OBJECTIVES: To develop a spatially- and temporally-stratified case-crossover approach for delineation of areas with higher and lower risk of mortality on extremely hot days, and to apply it in greater Vancouver, Canada. METHODS: Records of all deaths with an extremely hot day as a case day or a control day were extracted from an administrative vital statistics database spanning the years of 1998-2014. Three heat exposure and eleven social vulnerability variables were assigned at the residential location of each decedent. Conditional logistic regression was used to estimate the odds ratio for a 1 degrees C increase in daily mean temperature from a fixed site with an interaction term for decedents living above and below different values of the spatial variables. RESULTS: The heat exposure and social vulnerability variables with the strongest spatially-stratified results were the apparent temperature and the labor nonparticipation rate. Areas at higher risk had values >/=34.4 degrees C of the maximum apparent temperature and >/=60% of the population neither employed nor looking for work. These variables were combined in a composite index to quantify their interaction and enhance visualization of high-risk areas. CONCLUSIONS: Our methods provide a data-driven framework for spatial delineation of the temperature-mortality relationship by heat exposure and social vulnerability. Results can be used to map and target the most vulnerable areas for public health intervention.