Lowest temperature in Dallas on May 29, 2026?

Prediction market odds on Kalshi reflect real-money trader consensus and typically incorporate the latest meteorological models and seasonal forecasts. Unlike traditional analyst predictions, which may be published weeks or months in advance, market prices update continuously as new weather data arrives. Comparing Kalshi odds to National Weather Service forecasts or long-range climate outlooks can reveal whether traders are pricing in more bullish or bearish temperature scenarios than official guidance suggests.

On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. On Kalshi, this event is priced as a set of binary or range-based contracts representing specific low-temperature thresholds for Dallas on May 29, 2026. Traders buy and sell shares at prices between 0 and 100 cents, with each contract settling to 100 cents if the outcome occurs or 0 cents if it does not. The current market price reflects the collective probability estimate, and spreads between bid and ask prices indicate liquidity and trader conviction around each temperature band.

The market resolves on May 30, 2026, after the calendar day of May 29, 2026 concludes and official temperature data is recorded. Resolution is determined by the lowest temperature reading in Dallas on that specific date, sourced from authoritative meteorological records. Once the final low-temperature observation is confirmed and published, the market settles according to which outcome bracket or threshold the actual reading falls into.

Major weather pattern shifts, updated seasonal forecasts, and long-range climate models will drive price movement leading up to May 29, 2026. Cold fronts, high-pressure systems, and anomalies in jet-stream positioning can significantly alter overnight low temperatures. Additionally, broader climate trends, El Niño or La Niña conditions, and historical May weather patterns for Dallas will inform trader positioning. As May approaches, short-range forecasts become more precise, typically causing sharper repricing in the final weeks.