Lowest temperature in Dallas on Jul 10, 2026?

Prediction market odds reflect aggregated trader expectations and often diverge from traditional weather forecasts issued by meteorologists and climate analysts. While meteorologists rely on physics-based models and historical data, prediction markets incorporate real-time information, trader expertise, and financial incentives for accuracy. This market allows you to compare consensus pricing against published forecasts from the National Weather Service and other official sources. Traders who believe analyst predictions underestimate or overestimate the likelihood of specific temperature ranges can profit by taking opposing positions, creating a dynamic pricing mechanism distinct from one-way expert opinion.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares corresponding to different temperature outcomes. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each share represents a claim on a specific temperature range, and the market price of each outcome reflects the collective belief of active traders. As new weather data, seasonal patterns, or atmospheric conditions emerge, traders adjust their positions, moving prices up or down. The bid-ask spread represents the difference between what buyers and sellers are willing to pay, and tighter spreads typically indicate higher confidence and liquidity around particular outcomes.

This market resolves around Jul 11, 2026, once July 10, 2026 has passed and the lowest temperature recorded in Dallas that day is confirmed. The outcome is determined by verified weather data from credible public sources, ensuring an objective and transparent settlement. Traders' positions are settled based on which temperature range the actual low falls into, with payouts distributed accordingly. Until resolution, you can continue trading your position, allowing you to adjust your exposure as new forecasts and real-time conditions become available.

Several factors can shift market prices before this market resolves. Major weather pattern changes, such as the development of high-pressure systems or cold fronts, typically trigger significant repricing. Seasonal climate anomalies, El Niño or La Niña conditions, and long-range forecast updates from meteorological agencies can all influence trader positioning. Additionally, historical temperature records for early July in Dallas provide context that traders monitor. Real-time atmospheric data, upper-level wind patterns, and any unusual heat or cold events in surrounding regions may prompt traders to reassess their bets on the final outcome.