Lowest temperature in San Antonio on Jul 5, 2026?

Prediction market odds reflect real-money trading by participants who profit or lose based on accuracy, creating a financial incentive to incorporate the best available information. In contrast, traditional weather analysts and meteorologists rely on physics-based models and historical data but do not face direct financial consequences for their forecasts. This market aggregates dispersed knowledge from many traders, often revealing consensus or disagreement with official forecasts months in advance. Comparing the two can highlight where expert opinion and market expectations diverge on the likely low temperature for that July date.

On Kalshi, this market is priced through a continuous order-book mechanism where traders submit bids and offers on outcome shares. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The price of each outcome reflects the marginal probability assigned by the last matched trade, so as new information arrives—seasonal forecasts, climate patterns, or real-time weather updates—traders adjust their positions and prices shift accordingly. Higher prices indicate stronger market confidence in that outcome, while lower prices suggest lower perceived likelihood. This dynamic pricing ensures the market remains responsive to changing conditions leading up to July 5, 2026.

This market resolves around Jul 6, 2026, once the lowest temperature recorded in San Antonio on July 5, 2026 is verifiable from credible public sources. The outcome is determined by comparing the actual minimum temperature observed that day against the predefined threshold or range specified in the market terms. Once the event occurs and data is confirmed, the market settles automatically based on which outcome bracket the verified temperature falls into, and traders' positions are paid out accordingly.

Several factors could shift market odds significantly. Long-range seasonal forecasts and climate pattern updates from meteorological agencies often trigger repricing as traders adjust for shifts in expected summer conditions. El Niño or La Niña developments, changes in atmospheric circulation patterns, and historical temperature anomalies for early July in Texas all influence expectations. As the date approaches, short-range weather models become more reliable and can cause sharp moves. Additionally, any unusual heat waves or cold snaps in the region during late spring and early summer may prompt traders to reassess the likelihood of extreme temperatures on that specific day.