Highest temperature in San Francisco on June 30?

Prediction market prices reflect the aggregated beliefs of traders betting real money, which often diverge from traditional weather forecasts issued by meteorologists or climate analysts. While a meteorologist might issue a point estimate or confidence interval based on atmospheric models, this market prices uncertainty through supply and demand—traders who believe the outcome will differ from consensus can profit by taking the opposite side. Over time, prediction markets have proven competitive with or superior to expert forecasts in many domains, though they remain most reliable when close to the resolution date and when trading volume is active.

On Polymarket, traders set prices by buying and selling shares in each outcome contract, with the current price reflecting the implied probability that outcome will occur. On Polymarket, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each contract trades independently, so if one temperature range appears undervalued relative to others, arbitrage opportunities can emerge. The platform uses an automated market maker or order-book model to match buyers and sellers, and prices update continuously as new trades execute. Higher trading volume typically narrows bid-ask spreads and improves price discovery.

This market resolves around Jun 30, 2026, once the highest temperature recorded in San Francisco on June 30 is verified and confirmed. The outcome is determined by comparing the actual peak temperature reading against the predefined outcome ranges offered in the market. Resolution occurs after the event has concluded and credible public reporting—such as data from the National Weather Service or other recognized meteorological sources—has established the final figure. Traders holding shares in the winning outcome receive their payout once the market is settled.

Updated weather forecasts and seasonal climate patterns are primary drivers of price movement in this market. As the June 30 date approaches, meteorologists typically issue more precise predictions, which traders incorporate into their positions. Broader climate anomalies—such as an early heat wave or unusual cooling trend affecting California—can shift expectations significantly. Real-time atmospheric data, ocean temperature patterns, and historical precedent for Bay Area June weather all influence how traders reassess probabilities. Trading volume and media attention around extreme weather events can also amplify price swings as new participants enter the market.