I apply a novel machine-learning based “weather index” method to daily store- level sales data for a national apparel and sporting goods brand to examine short-run responses to weather and long-run adaptation to climate. I find that even when considering potentially offsetting shifts of sales between outdoor and indoor stores, to the firm’s website, or over time, weather has significant persistent effects on sales. This suggests that weather may increase sales volatility as more severe weather shocks be- come more frequent under climate change. Consistent with adaptation to climate, I find that sensitivity of sales to weather decreases with historical experience for precipitation, snow, and cold weather events, but-surprisingly-not for extreme heat events. This suggests that adaptation may moderate some but not all of the adverse impacts of climate change on sales. Retailers can respond by adjusting their staffing, inventory, promotion events, compensation, and financial reporting.