Lowest temperature in San Francisco on Jun 21, 2026?

Prediction market odds reflect real-money consensus from traders betting on the outcome, whereas traditional analyst forecasts rely on meteorological models and historical climate data. This market aggregates distributed knowledge from many participants, often capturing nuanced shifts in seasonal expectations faster than static forecasts. Comparing the odds here to published weather outlooks for early summer in San Francisco can reveal where the market is pricing in tail risks or confidence gaps that analysts may underweight. Both approaches offer value: markets excel at synthesizing uncertainty, while expert forecasts provide detailed physical reasoning.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares representing different temperature ranges or outcomes. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Prices move based on supply and demand; as more traders expect a particular outcome, the odds for that outcome rise, and contrarian positions become cheaper. The platform displays bid-ask spreads in real time, allowing you to enter at competitive rates. Kalshi's pricing model ensures transparent, market-driven discovery of the true probability that San Francisco's low temperature on June 21, 2026 will fall within each specified band.

This market resolves around Jun 22, 2026, once June 21, 2026 has passed and the day's minimum temperature is verifiable from credible public sources. The outcome is determined by the lowest temperature recorded in San Francisco on that date, confirmed against official meteorological data. Resolution typically occurs within hours or a day after the event, allowing traders to settle positions promptly. The specific temperature threshold or range that wins depends on the market's outcome categories, which partition the full range of plausible lows into discrete, mutually exclusive options.

Several factors could shift odds significantly before resolution. Long-range climate forecasts and seasonal outlooks released by NOAA or other meteorological agencies often trigger repricing as traders incorporate new data. El Niño or La Niña patterns, Pacific Ocean temperatures, and historical June weather trends in San Francisco all influence expectations. Major weather events or anomalies in the months leading up to June 2026 may alter baseline assumptions about typical summer lows. Additionally, as the event date approaches, real-time weather models become more precise, typically narrowing uncertainty and sharpening the market's price discovery around the most likely outcome.