Although many studies have examined the effects of heat waves on the excess mortality risk (ER) posed by cardiovascular disease (CVD), scant attention has been paid to the effects of various combinations of differing heat wave temperatures and durations. We investigated such effects in Beijing, a city of over 20 million residents. A generalized additive model (GAM) was used to analyze the ER of consecutive days’ exposure to extreme high temperatures. A key finding was that when extremely high temperatures occur continuously, at varying temperature thresholds and durations, the adverse effects on CVD mortality vary significantly. The longer the heat wave lasts, the greater the mortality risk is. When the daily maximum temperature exceeded 35 °C from the fourth day onward, the ER attributed to consecutive days’ high temperature exposure saw an increase to about 10% (p < 0.05), and at the fifth day, the ER even reached 51%. For the thresholds of 32 °C, 33 °C, and 34 °C, from the fifth day onward, the ER also rose sharply (16, 29, and 31%, respectively; p < 0.05). In addition, extreme high temperatures appeared to contribute to a higher proportion of CVD deaths among elderly persons, females and outdoor workers. When the daily maximum temperature was higher than 33 °C from the tenth consecutive day onward, the ER of CVD death among these groups was 94, 104 and 149%, respectively (p < 0.05), which is considerably higher than the ER for the overall population (87%; p < 0.05). The results of this study may assist governments in setting standards for heat waves, creating more accurate heat alerts, and taking measures to prevent or reduce temperature-related deaths, especially against the backdrop of global warming.

Minimum mortality temperature (MMT) is an important indicator to assess the temperature–mortality relationship. It reflects human adaptability to local climate. The existing MMT estimates were usually based on case studies in data rich regions, and limited evidence about MMT was available at a global scale. It is still unclear what the most significant driver of MMT is and how MMT will change under global climate change. Here, by analysing MMTs in 420 locations covering six continents (Antarctica was excluded) in the world, we found that although the MMT changes geographically, it is very close to the local most frequent temperature (MFT) in the same period. The association between MFT and MMT is not changed when we adjust for latitude and study year. Based on the MFT~MMT association, we estimate and map the global distribution of MMTs in the present (2010s) and the future (2050s) for the first time.