Lowest temperature in San Antonio on Jul 11, 2026?

Prediction market odds reflect the aggregated beliefs of traders with real money at stake, often incorporating meteorological models and historical temperature data faster than traditional analyst consensus. While weather forecasters rely on physics-based simulations and satellite imagery, this market prices in trader expectations about measurement uncertainty and local microclimatic factors. Over time, markets tend to converge toward verifiable outcomes, making them a useful cross-check against published forecasts from the National Weather Service and other official sources.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares corresponding to different temperature ranges or point estimates. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each share represents a claim on the outcome, and the market price reflects the collective probability assigned by active participants. As new information arrives—updated weather models, seasonal anomalies, or real-time conditions—traders adjust their positions, causing prices to move and the implied probability to shift in real time.

This market resolves around Jul 12, 2026, once the lowest temperature recorded in San Antonio on July 11, 2026 is verifiable from credible public sources. The outcome is determined by the actual observed low temperature on that date, confirmed through official weather station data and historical records. Traders' positions settle based on whether their prediction falls within or outside the correct temperature range or threshold specified by the market rules.

Major weather pattern shifts, seasonal climate anomalies, and updated long-range forecasts from meteorological agencies can significantly influence trader positioning. El Niño or La Niña conditions, jet stream behavior, and historical temperature records for early July in San Antonio all serve as key inputs. As the event date approaches, real-time weather models become more precise, often triggering sharp repricing. Additionally, any unusual atmospheric events or heat waves affecting the broader region may prompt traders to reassess their bets on the final low temperature.