Lowest temperature in San Antonio on Jul 3, 2026?

Prediction market odds reflect the collective judgment of traders betting real money on outcomes, whereas meteorological forecasts rely on physics-based climate models and historical temperature patterns. This market aggregates dispersed trader knowledge into a single price signal, which often incorporates information faster than traditional analyst updates. Comparing the implied probability from this market to published seasonal forecasts for South Texas can reveal where traders expect conditions to diverge from conventional models, offering a complementary perspective on July temperature expectations.

On Kalshi, this market is priced through an order-book mechanism where traders submit bids and asks for contracts tied to specific temperature ranges or thresholds. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The price of each contract reflects the probability traders assign to that outcome, with higher prices indicating greater confidence. As new weather data, climate patterns, or seasonal updates surface, traders adjust their orders, causing the market price to move continuously until resolution.

This market resolves around Jul 4, 2026, once the lowest temperature recorded in San Antonio on July 3, 2026 is verified and confirmed. The outcome is determined by credible public reporting of actual weather observations from that date. Traders holding contracts aligned with the verified low temperature receive their payout, while positions on other outcomes expire worthless.

Major shifts in seasonal climate forecasts, updates to long-range weather models, and historical temperature anomalies for early July in Texas can all influence trader positioning. El Niño or La Niña patterns, changes in atmospheric circulation, and any unusual heat or cold events in the preceding months may prompt traders to reassess their probability estimates. As July 3, 2026 approaches, daily and weekly forecasts will become more precise, likely triggering sharp repricing as the actual weather window narrows and uncertainty declines.