Lowest temperature in San Antonio on Jul 8, 2026?

Prediction market odds reflect aggregated trader expectations and often diverge from traditional meteorological forecasts. While weather analysts issue point estimates and confidence intervals based on atmospheric models, this market prices uncertainty through continuous trading. Traders incorporate real-time data, historical patterns, and model consensus into their bids, sometimes leading markets to discount or amplify specific outcomes relative to published forecasts. Over time, prediction markets have shown competitive accuracy against expert predictions, particularly when traders have access to the same underlying data sources.

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 contract trades independently, with the bid-ask spread reflecting uncertainty and liquidity depth. Prices move as new information arrives and traders adjust positions, with the most liquid outcomes typically showing tighter spreads. The platform's matching engine ensures transparent price discovery and allows participants to enter or exit positions at any time before the market closes.

This market resolves around Jul 9, 2026, once the lowest temperature recorded in San Antonio on July 8, 2026 is verified against credible public sources. The outcome is determined by the actual low temperature reported for that date, measured in degrees Fahrenheit. Traders holding shares in the correct outcome range receive their payout once the event is confirmed and the market is officially settled. Resolution typically occurs within hours of the close of the trading day.

This market will respond to updated seasonal forecasts, atmospheric pressure systems, and long-range weather models as they refine predictions for early July 2026. Major climate patterns—such as shifts in jet stream positioning or tropical activity—can shift trader expectations significantly. Real-time weather data closer to the event date, including satellite imagery and ground observations, will likely trigger sharp repricing. Historical temperature records for San Antonio in early July and any unusual atmospheric anomalies announced by meteorological agencies could also drive material moves in implied probabilities.