Lowest temperature in San Antonio on Jun 23, 2026?

Prediction market odds often diverge from traditional weather analyst forecasts because they incorporate real-time trader behavior, uncertainty premiums, and dynamic information flow. While meteorologists issue deterministic or probabilistic forecasts based on atmospheric models, this market reflects the aggregated beliefs of participants who have financial incentives to forecast accurately. Traders may price in tail risks or consensus shifts faster than official forecasts update. Comparing the implied probabilities here to published forecasts from the National Weather Service or private meteorology firms can reveal where the crowd sees asymmetric risk or where expert opinion lags emerging data.

On Kalshi, this market is priced through a continuous order-book mechanism where traders submit bids and asks for shares tied to specific temperature outcome ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The platform matches buyers and sellers in real time, and the midpoint or last-traded price reflects the current market consensus on the lowest temperature. Each outcome bucket has its own price, and traders profit or lose based on whether their chosen range contains the actual low temperature on the event date. Prices move as new information arrives and trading interest shifts across different temperature thresholds.

This market resolves around Jun 24, 2026, once the lowest temperature recorded in San Antonio on June 23, 2026 is verifiable from credible public sources. The outcome is determined by the actual minimum temperature reading for that day, confirmed through official weather station data or recognized meteorological records. Traders holding shares in the outcome range that contains the verified low temperature receive their payout, while other positions expire worthless. Resolution typically occurs within hours of the close of the trading day once the final temperature is recorded and published.

Major weather pattern shifts, seasonal forecasts, and updated climate models will drive significant price swings in this market. Tropical systems, heat domes, or cold fronts affecting Texas in the weeks leading up to June 23 could reshape trader expectations. Real-time atmospheric data, including jet stream positioning and sea-surface temperatures, often triggers repricing as meteorological confidence changes. Historical temperature records for that date and time of year provide a baseline, but anomalies or emerging climate trends may push traders toward higher or lower temperature outcomes. News of drought, El Niño, or La Niña patterns can also influence longer-term positioning.