Highest temperature in San Francisco on Jul 11, 2026?

Prediction market odds reflect the collective judgment of traders betting real money on outcomes, whereas traditional analyst forecasts rely on meteorological models and expert opinion. This market aggregates dispersed information from many participants, often capturing signals that individual forecasters might miss. Comparing the implied probability in this market to published climate and weather forecasts can reveal where the crowd expects conditions to diverge from baseline expectations. Both approaches have merit; markets excel at synthesizing uncertainty, while meteorological models provide detailed physical reasoning.

On Kalshi, this market is priced through an order-book mechanism where traders buy and sell shares corresponding to different temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome is represented as a separate contract, and the price of each reflects the probability traders assign to that outcome occurring. As new information arrives—updated weather models, seasonal trends, or real-time atmospheric data—traders adjust their bids and asks, moving prices dynamically. The spread between buy and sell prices narrows as more participants trade, improving price discovery.

This market resolves around Jul 12, 2026, once July 11, 2026 has passed and the highest temperature recorded in San Francisco that day can be verified. The outcome is confirmed against credible public reporting of official temperature data. Traders holding shares in the outcome that matches the verified high temperature receive their payout, while other positions expire worthless. Resolution typically occurs within hours of the market close, pending final data confirmation.

Major shifts in long-range weather forecasts, updated climate models, and seasonal pattern changes will influence trading activity in this market. El Niño or La Niña conditions, atmospheric pressure systems, and historical temperature anomalies for early July in San Francisco are key catalysts. Real-time developments such as heat waves affecting the broader West Coast, changes in ocean temperatures, or unexpected atmospheric events can trigger rapid repricing. As the date approaches, short-term weather forecasts become more reliable and often drive sharper price movements closer to resolution.