Lowest temperature in San Francisco on Jun 12, 2026?

Prediction market odds reflect real-money incentives, meaning traders who misjudge conditions face direct financial loss. This differs from traditional analyst forecasts, which carry no personal stake. In this market, participants incorporate meteorological models, historical June patterns for San Francisco, and emerging climate signals into their positions. When professional forecasters and prediction market traders converge on similar probabilities, it often signals high confidence. Divergence between the two can highlight where expert opinion and market pricing disagree, potentially revealing undervalued or overvalued outcomes.

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. Each share reflects the crowd's collective belief about the probability of that outcome occurring. As new weather data, seasonal trends, or forecasts emerge, traders adjust their positions, moving prices up or down. The bid-ask spread represents the gap between buyers and sellers, and tighter spreads typically indicate higher confidence and liquidity in the market.

This market resolves around Jun 13, 2026, after June 12, 2026 has passed. The outcome is determined by the verified lowest temperature recorded in San Francisco on that date, confirmed once the event is observable from credible public reporting. Traders holding positions aligned with the actual low temperature receive their payout, while those on the wrong side of the outcome realize losses. Resolution typically occurs within hours or a day after the event, once official data is published and the platform confirms the result.

Major weather pattern shifts, updated seasonal forecasts, and climate anomalies can significantly move this market. El Niño or La Niña developments, changes in Pacific Ocean temperatures, and atmospheric pressure systems will influence trader expectations. Unexpected cold fronts or heat waves in late spring could trigger sharp repricing. Additionally, historical comparisons to previous June temperatures in San Francisco and real-time meteorological model updates will drive trading activity. As the date approaches, short-term forecasts become more reliable, typically narrowing the range of plausible outcomes and stabilizing prices.