Lowest temperature in San Antonio on Jul 1, 2026?

Prediction market odds reflect real-money bets from traders and often diverge from traditional meteorological forecasts. While weather analysts rely on numerical models and historical climate data to project conditions months ahead, this market aggregates the views of many participants with financial incentives to be accurate. Prediction markets can sometimes detect signals earlier than consensus forecasts, though both approaches have strengths—analysts bring domain expertise, while markets reward those who correctly anticipate outcomes. Comparing the two provides a richer picture of what to expect for San Antonio's low temperature that day.

On Kalshi, this market is priced through an order-book mechanism where traders submit bids and asks for shares corresponding to different temperature outcomes. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The price of each outcome reflects supply and demand; as more traders believe a particular temperature range is likely, the cost of shares in that outcome rises. You can enter limit or market orders to buy or sell shares, and your position's value updates in real time as the market reprices. Settlement occurs after the event, with payouts determined by which outcome actually occurred.

This market resolves around Jul 2, 2026, once July 1, 2026 has passed and the lowest temperature in San Antonio for that day is verified. The outcome is confirmed against credible public weather reporting and historical temperature records. Traders holding shares in the outcome that matches the verified low temperature receive their payout, while other positions expire worthless. The exact resolution hinges on which temperature bracket or specific value the official data confirms.

Several factors can shift odds in this market over the coming months. Major climate pattern changes—such as shifts in the jet stream, El Niño or La Niña conditions, or unusual high-pressure systems—can alter seasonal temperature expectations. Updated long-range forecasts from meteorological agencies often trigger repricing as traders incorporate new data. Unusual heat waves or cold snaps in nearby regions may also influence sentiment. As July 1, 2026 approaches, near-term weather models become more reliable, typically causing sharper price movements. Real-time trading activity and shifts in trader conviction will continuously reshape the odds.