Lowest temperature in Denver on Jun 22, 2026?

Prediction market odds often diverge from traditional weather analyst forecasts because they incorporate real-time trader behavior, financial incentives, and a broader range of information sources. While meteorologists rely on atmospheric models and historical climate data, this market prices in sentiment from thousands of participants betting real money on the outcome. Comparing the odds here to published forecasts from the National Weather Service or other agencies can reveal whether traders are pricing in more optimistic or pessimistic scenarios than official predictions suggest, offering a complementary perspective on expected conditions.

On Kalshi, this market is priced through an order-book mechanism where traders buy and sell shares representing different temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome is assigned a price between 0 and 100 cents, reflecting the implied probability that the low will fall within that range. As traders place bids and asks, the price adjusts in real time to balance supply and demand, ensuring the market continuously reflects the latest consensus on Denver's temperature low for June 22, 2026.

This market resolves around Jun 23, 2026, once the event has occurred and the lowest temperature for June 22, 2026 in Denver can be verified from credible public sources. The outcome is determined by comparing the actual recorded low to the temperature thresholds defined by each outcome option in the market. Traders holding shares in the correct outcome receive their payout, while incorrect positions expire worthless.

Several factors may shift odds in this market over time. Long-range seasonal forecasts and climate pattern updates from meteorological agencies can trigger significant repricing, particularly as June 2026 approaches. El Niño or La Niña conditions, historical temperature anomalies for that date, and shifts in atmospheric circulation patterns all influence trader expectations. Additionally, as the actual date draws closer, short-term weather models become more reliable, often causing sharper price movements as traders adjust positions based on improved forecast confidence and newly available data.