Highest temperature in Singapore on June 27?

Prediction market odds often diverge from traditional meteorological forecasts because they incorporate real-time trader sentiment and broader information flows beyond standard weather models. While meteorologists rely on atmospheric data and historical patterns, this market aggregates the beliefs of many participants with financial incentives to forecast accurately. Comparing the odds here to published forecasts from Singapore's Meteorological Service or regional weather agencies can reveal where traders expect conditions to differ from baseline expectations, making the market a useful cross-check against conventional analysis.

On Polymarket, traders set prices through an automated market maker that balances supply and demand for each temperature outcome. On Polymarket, 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 price of a share reflects the probability the market assigns to that range occurring. As new information emerges—such as updated weather models or real-time atmospheric conditions—traders adjust their positions, moving prices up or down. This continuous repricing mechanism ensures the market reflects the latest available evidence about Singapore's weather on June 27.

This market resolves around Jun 27, 2026, once the highest temperature recorded in Singapore on that date becomes verifiable from credible public sources. The outcome is determined by comparing actual recorded temperatures against the predefined ranges offered in the market. Once the event occurs and the official temperature is confirmed, the market settles automatically, with winning shares paying out and losing shares expiring worthless. Traders holding positions in the correct temperature range receive their winnings based on the odds at which they entered.

Several factors could shift odds in this market before Jun 27, 2026. Updated weather models from meteorological agencies, tropical storm systems approaching the region, or shifts in seasonal climate patterns could all trigger repricing. Real-time atmospheric data, ocean temperature anomalies, and monsoon intensity forecasts may influence trader expectations. Additionally, any significant weather events in nearby regions or changes to historical temperature records could alter the probability distribution. Traders monitoring these signals continuously adjust positions, so major weather announcements or model updates typically produce visible price movements in the market.