Lowest temperature in Oklahoma City on Jun 21, 2026?

Prediction market odds often diverge from traditional weather analyst forecasts because they incorporate real-time trader conviction and aggregate dispersed information across many participants. While meteorologists issue point forecasts or confidence intervals based on atmospheric models, this market prices uncertainty through continuous trading. Traders betting on lower temperatures may signal skepticism of warmer consensus forecasts, or vice versa. Over time, prediction markets have demonstrated competitive accuracy against institutional forecasts by rewarding traders who identify model blind spots or emerging data. Comparing the current odds to published National Weather Service or private meteorology firm predictions can reveal where market participants expect surprises.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares corresponding to specific temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome bucket has its own bid-ask spread, and the price of a share reflects the collective probability assigned by the market to that temperature occurring. Traders profit by correctly predicting which range will verify as the actual low, incentivizing accurate pricing. The platform's matching engine executes trades instantly, and prices adjust in real time as new information arrives or sentiment shifts among participants.

This market resolves around Jun 22, 2026, once June 21, 2026 has passed and the lowest temperature for that day in Oklahoma City is verifiable from credible public sources. The outcome is determined by comparing the recorded low temperature against the predefined outcome ranges offered in the market. Traders holding shares in the correct range receive their payout, while incorrect positions expire worthless. Resolution typically occurs within hours of the event, pending confirmation from authoritative weather data.

Major weather pattern shifts, seasonal climate anomalies, and updated long-range forecasts from meteorological agencies can significantly move odds in this market. Unusual atmospheric conditions—such as early heat waves, cold fronts, or tropical systems—may trigger rapid repricing as traders adjust for new risk. Historical temperature records and climate data releases can also influence expectations. As June 21 approaches, short-range model consensus typically tightens, reducing uncertainty and stabilizing prices. Real-time updates from the National Weather Service or significant deviations from seasonal norms will attract fresh trading activity and shift the probability distribution across outcome buckets.