A growing literature estimates the macroeconomic effect of weather using variations in annualcountry-level averages of temperature and precipitation. However, averages may not reveal the effects ofextreme events that occur at a higher time frequency or higher spatial resolution. To address this issue, we relyon global daily weather measurements with a 30-km spatial resolution from 1979 to 2019 and construct 164weather variables and their lags. We select a parsimonious subset of relevant weather variables using analgorithm based on the Least Absolute Shrinkage and Selection Operator. We also expand the literature byanalyzing weather impacts on government revenue, expenditure, and debt, in addition to GDP per capita. Wefind that an increase in the occurrence of high temperatures and droughts reduce GDP, whereas more frequentmild temperatures have a positive impact. The share of GDP variations that is explained by weather ascaptured by the handful of our selected variables is much higher than what was previously implied by usingannual temperature and precipitation averages. We also find evidence of counter-cyclical fiscal policies thatmitigate adverse weather shocks, especially excessive or unusually low precipitation episodes.