Highest temperature in San Francisco on Jun 24, 2026?

Prediction market odds often diverge from traditional weather forecasts and analyst models because they incorporate real-time trader sentiment, historical volatility, and broader economic or climate signals beyond standard meteorological data. While meteorologists rely on physics-based models and historical patterns, this market aggregates the financial incentives of traders who profit from accurate predictions. Over time, prediction markets have demonstrated competitive accuracy against expert forecasts, particularly when traders have access to diverse information sources. Comparing odds here to published weather forecasts for June 2026 can reveal where market participants see upside or downside risk relative to conventional expectations.

On Kalshi, this market is priced through a continuous order book where traders buy and sell shares corresponding to different temperature outcomes. On Kalshi, 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 each outcome reflects the probability traders assign to it occurring. As new information arrives—updated climate data, seasonal patterns, or real-time weather developments—traders adjust their positions, moving prices up or down. The spread between bid and ask prices narrows or widens based on trading activity and conviction, with tighter spreads indicating higher confidence and liquidity around certain outcomes.

This market resolves around Jun 25, 2026, once June 24, 2026 has passed and the highest temperature recorded in San Francisco that day is verified against credible public sources. The outcome is determined by the official temperature reading from a recognized meteorological authority, ensuring an objective and transparent settlement. Traders holding shares in the correct outcome receive their payout once verification is complete. The resolution process is designed to be straightforward and dispute-resistant, anchored to independently verifiable data.

Several factors could shift odds significantly before resolution. Updated seasonal climate forecasts, El Niño or La Niña patterns, and long-range weather model runs published closer to June 2026 will influence trader positioning. Real-time atmospheric conditions in May and early June—heat waves, marine layer intensity, or unusual pressure systems—could trigger sharp repricing. Historical temperature records and anomalies for that date may also prompt traders to adjust expectations. Additionally, any major climate or weather events affecting California's broader climate patterns could reshape how traders assess the probability of extreme heat in San Francisco on that specific day.