Abstract: It has been agreed that adverse temperature can increase cardiovascular mortality. However, the role and economic value of weather forecasts in moderating the exposure response relationship between temperature and mortality have not been reported. Therefore, based on observation data collected at Beijing meteorological station (No.54511) and daily cardiovascular mortality in Beijing, this study tests the accuracy of temperature forecasts released by the Beijing Meteorological Observatory from 2006 to 2016, constructs an exposure response relationship between temperature and cardiovascular morbidity in Beijing by the distribution lag model under three scenarios, i.e., when temperature is accurately predicted, overestimated and underestimated, and evaluates the health effect of weather forecast based on subjective estimate. Finally, the adjusted human capital method is used to estimate the economic value of weather forecasts in reducing the health risk of cardiovascular morbidity. The results show that the accuracy of the daytime maximum temperature has been significantly improved in the past 11 years, and the accuracy based on the deviation of no more than 2℃ has increased from 60% to 84%. The study of the exposure response relationship shows that the maximum daily relative risk caused by adverse temperature can be reduced when it is accurately predicted. Based on the assumption that people would subjectively estimate temperature from actual temperature of the previous day, it is found that the accuracy of subjective prediction basically remains stable from 2006 to 2016. According to the differences between weather forecasts and subjective estimates, it is recognized that the maximum daily excess death toll of cardiovascular morbidity reduced by the weather forecast in Beijing showed an upward trend from 2006 to 2016, and the annual decrease was between 114 and 457. Economic evaluation reveals that the economic value of the health impact of temperature prediction on cardiovascular morbidity in Beijing increased from 121 million yuan in 2006 to 1131 million yuan in 2016.