  Recent literature has suggested that climate conditions have considerably
significant influences on the transmission of coronavirus COVID-19. However,
there is a lack of comprehensive study that investigates the relationships
between multiple weather factors and the development of COVID-19 pandemic while
excluding the impact of social factors. In this paper, we study the
relationships between six main weather factors and the infection statistics of
COVID-19 on 250 cities in Mainland China. Our correlation analysis using
weather and infection statistics indicates that all the studied weather factors
are correlated with the spread of COVID-19, where precipitation shows the
strongest correlation. We also build a weather-aware predictive model that
forecasts the number of infected cases should there be a second wave of the
outbreak in Mainland China. Our predicted results show that cities located in
different geographical areas are likely to be challenged with the second wave
of COVID-19 at very different time periods and the severity of the outbreak
varies to a large degree, in correspondence with the varying weather
conditions.
