Lowest temperature in San Francisco on Jun 30, 2026?

Prediction market odds reflect real-money trader positions and incentives, often diverging from traditional meteorologist forecasts. While weather analysts rely on physics-based models and historical patterns, this market aggregates dispersed information from participants who profit only if their temperature prediction proves correct. Over time, prediction markets have shown competitive accuracy against expert consensus, particularly when traders have access to the same underlying data. Comparing current odds to published seasonal outlooks can reveal where the market sees asymmetric risk or unique conviction.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares corresponding to different temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome contract reflects the probability traders assign to that range occurring, with the price ranging from near-zero to near-100 cents per share. Liquidity and trading volume determine how easily you can enter or exit positions at any given moment. The platform's matching engine ensures transparent price discovery as new information about June weather patterns becomes available.

This market resolves around Jul 1, 2026, once the lowest temperature recorded in San Francisco on June 30, 2026 is verifiable from credible public sources. The outcome is determined by official meteorological data, ensuring an objective and dispute-free settlement. Traders holding shares in the correct temperature range receive their payout once the event passes and the result is confirmed. The resolution process is automated and transparent, with all participants seeing the same authoritative data.

Major climate patterns, seasonal forecasts, and long-range weather models will drive significant price shifts in this market. El Niño or La Niña conditions, atmospheric pressure systems, and historical June temperature trends for the Bay Area all influence trader positioning. As the event date approaches, updated meteorological outlooks and real-time weather data will tighten probability ranges. Unusual heat waves or cold snaps elsewhere in North America may also shift expectations for San Francisco's microclimate, prompting rapid repricing of outcome odds.