Lowest temperature in San Antonio on Jun 13, 2026?

Prediction market odds reflect aggregated trader expectations and often diverge from traditional meteorologist forecasts because they incorporate real-time information, historical accuracy adjustments, and financial incentives for precision. While weather analysts publish deterministic point forecasts or confidence intervals based on numerical models, this market prices uncertainty through continuous trading. Traders who believe the consensus underestimates the likelihood of extreme cold or unseasonable warmth can profit by taking opposing positions, creating a dynamic pricing mechanism that sometimes leads or lags official forecasts depending on data availability and model updates.

On Kalshi, this market is priced through an order-book mechanism where traders buy and sell shares corresponding to each temperature outcome. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome is bounded by a specific temperature range, and the price of a share reflects the collective probability assigned by active traders. As new weather data emerges or the event date draws closer, traders adjust their positions, causing prices to shift. The platform ensures transparent price discovery by displaying the spread between bid and ask prices, allowing participants to enter or exit positions at market rates or place limit orders for execution when conditions align.

This market resolves around Jun 14, 2026, once June 13, 2026 has passed and the lowest temperature recorded in San Antonio that day is verified against credible public sources. The outcome is determined by comparing the actual low temperature to the predefined outcome brackets established at market creation. Traders holding shares in the outcome bracket that contains the verified low temperature receive their payout, while other positions expire worthless. Resolution is final once the temperature data is confirmed and the market is settled on the platform.

Major atmospheric patterns, seasonal climate anomalies, and updated long-range weather models are key catalysts that shift market pricing. El Niño or La Niña conditions, polar vortex behavior, and jet stream positioning influence summer temperatures across Texas. As the event date approaches, medium-range forecasts become more reliable, typically causing price volatility to increase as traders incorporate higher-confidence data. Historical heat waves or cold snaps in the region may also prompt traders to reassess tail-risk outcomes. Real-time weather alerts, atmospheric pressure systems, and any unusual climate events in the weeks leading up to June 13, 2026 can trigger rapid repricing of outcome probabilities.