Highest temperature in Busan on June 30?

Prediction market odds often diverge from traditional weather forecasts because they incorporate real-time trader conviction and aggregate dispersed information beyond official meteorological models. While meteorologists issue point estimates and confidence intervals, this market reflects what participants are willing to stake financially on specific temperature outcomes. Comparing the implied probabilities here to published forecasts from regional weather services can reveal where the crowd sees upside or downside risk. Such gaps sometimes signal emerging consensus about atmospheric conditions that official models haven't yet fully captured.

On Polymarket, traders set prices by buying and selling shares tied to distinct temperature brackets. On Polymarket, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome's price reflects the collective belief in its likelihood, with higher prices indicating stronger confidence. Liquidity pools or order books allow you to enter or exit positions at the prevailing market rate. As new weather data arrives or conditions shift, prices adjust dynamically to reflect updated expectations, ensuring the market remains responsive to real-world developments leading up to resolution.

This market resolves around Jun 30, 2026, once the highest temperature recorded in Busan on June 30 becomes verifiable from credible public sources. The outcome is determined by comparing actual meteorological readings against the temperature ranges offered in the market. Whichever bracket contains the official high temperature for that day settles as the winning outcome, and all shares in that bracket pay out at full value. Traders holding positions in other ranges receive nothing.

Major weather pattern shifts—such as tropical storm systems, monsoon onset timing, or high-pressure ridges—can significantly reprrice this market. Updated seasonal forecasts, atmospheric indices like sea-surface temperature anomalies, and real-time satellite imagery often trigger sharp moves as traders reassess probability. Unexpected cold fronts or heat waves in neighboring regions may also influence expectations. As June 30 approaches, short-range numerical weather models become more reliable, typically narrowing uncertainty and stabilizing prices. Breaking news about climate anomalies or regional weather events in the weeks prior can create sudden trading spikes.