Highest temperature in San Francisco on Jul 7, 2026?

Prediction market odds reflect aggregated trader expectations and often diverge from traditional meteorologist forecasts because markets incorporate real-time information, uncertainty premiums, and participant conviction. While weather analysts publish deterministic point forecasts or narrow ranges based on atmospheric models, this market prices a range of temperature outcomes with explicit probabilities. Comparing the two reveals whether traders expect conditions to be warmer or cooler than official guidance, and by how much. Over time, prediction markets have proven competitive with expert forecasts for weather events, particularly when liquidity is sufficient to attract informed participants.

On Kalshi, this market is priced through a continuous 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 bracket has its own price, typically expressed as a decimal between 0 and 1, reflecting the implied probability that the high temperature will fall within that range. Prices adjust in real time as new orders flow in, allowing traders to enter or exit positions at market rates. The spread between bid and ask prices narrows or widens depending on trading activity and conviction around specific temperature thresholds.

This market resolves around Jul 8, 2026, once the actual high temperature for July 7, 2026 in San Francisco is verified and confirmed. The outcome is determined by comparing the recorded high against the predefined temperature brackets offered in the market. Verification occurs through credible public sources such as official weather station data or recognized meteorological records. Traders holding shares in the outcome bracket that contains the verified high temperature receive their payout, while other positions expire worthless.

Major weather pattern shifts, updated seasonal forecasts, and atmospheric anomalies such as heat domes or marine layer changes could significantly move prices in this market. El Niño or La Niña conditions, historical heat waves in early July, and long-range climate models all influence trader expectations. Breaking news about record-setting temperatures in nearby regions or unusual jet stream positioning may trigger rapid repricing. As July 7, 2026 approaches, short-range forecasts from the National Weather Service become more precise, typically causing the market to converge toward narrower probability bands and sharper price movements.