Highest temperature in Wuhan on June 15?

Prediction market odds often diverge from traditional meteorological forecasts because they incorporate real-time trader sentiment and risk appetite alongside expert models. While meteorologists rely on atmospheric data and historical patterns, this market reflects what participants are willing to stake on various temperature outcomes. Comparing the implied probabilities here to published forecasts from weather agencies can reveal whether traders are pricing in more optimism or caution than official predictions suggest, making the market a useful cross-check on consensus expectations.

On Polymarket, traders set prices by buying and selling shares corresponding to different temperature ranges for June 15 in Wuhan. On Polymarket, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The cost of each share reflects the implied probability that outcome will occur; higher prices indicate stronger market confidence. As new weather data arrives or trading interest shifts, prices adjust continuously, allowing participants to enter or exit positions throughout the market's lifetime and ensuring prices remain responsive to evolving conditions.

This market resolves around Jun 15, 2026, once June 15 has passed and the highest temperature recorded in Wuhan that day can be verified. The outcome is confirmed against credible public sources reporting the actual peak temperature, then matched to the corresponding outcome range in the market. Traders holding shares in the correct temperature bracket receive their payout, while incorrect positions expire worthless.

Major weather pattern shifts, updated seasonal forecasts, or significant atmospheric anomalies could drive substantial price swings in this market. Real-time meteorological alerts, changes in regional climate conditions, or unexpected heat waves or cold snaps would prompt traders to reassess their positions. Additionally, as June 15 approaches, actual weather observations and short-term forecasts become more reliable, typically narrowing uncertainty and stabilizing prices around the most probable outcome range.