Highest temperature in San Francisco on Jul 9, 2026?

Prediction market odds often diverge from traditional meteorological forecasts because they incorporate real-time trader behavior, historical volatility, and longer-term climate signals alongside official weather models. While meteorologists typically issue forecasts days to weeks in advance with confidence intervals, this market aggregates the beliefs of many participants trading over months, potentially capturing tail risks and seasonal anomalies that standard models may underweight. Comparing the implied probabilities here to published climate outlooks and historical July temperature records for San Francisco can reveal whether traders are pricing in more extreme heat than experts currently project, or vice versa.

On Kalshi, this market is priced through an order-book mechanism where traders buy and sell shares corresponding to different temperature outcomes. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome has a bid-ask spread reflecting the current supply and demand for that temperature range. As new information emerges—updated climate forecasts, historical comparisons, or seasonal trends—traders adjust their positions, moving the price up or down. The market price at any moment represents the collective valuation of the likelihood that the highest temperature on July 9, 2026 will fall within each specified bracket.

This market resolves around Jul 10, 2026, once the highest temperature recorded in San Francisco on July 9, 2026 is verifiable from credible public sources. The outcome is determined by the actual peak temperature observed that day, confirmed through official weather station data or recognized meteorological records. Traders' positions settle based on which temperature bracket the final reading falls into, with payouts distributed accordingly. Resolution typically occurs within hours of the market close once the day's data is finalized and published.

Major shifts in long-range climate forecasts, updated seasonal outlooks from NOAA or other meteorological agencies, and historical heat-wave patterns could significantly move this market. El Niño or La Niña conditions, changes in Pacific sea-surface temperatures, and atmospheric blocking patterns are key drivers of Bay Area summer heat. As July 2026 approaches, shorter-term weather models will become more precise, likely triggering repricing. Additionally, any unusual heat events in prior summers or revisions to climate normals could influence trader expectations about what constitutes an extreme day for San Francisco in mid-summer.