Lowest temperature in San Francisco on Jul 9, 2026?

Prediction market odds reflect real-money trader conviction and often incorporate meteorological forecasts, historical temperature patterns, and seasonal trends. Unlike traditional analyst predictions, this market aggregates dispersed information through price discovery—traders who accurately forecast outcomes profit, creating strong incentives for accuracy. While meteorologists publish deterministic forecasts based on atmospheric models, market odds represent probabilistic consensus weighted by trader confidence and capital allocation. Both approaches offer value; markets excel at synthesizing uncertainty, while expert forecasts provide detailed reasoning.

On Kalshi, this market is priced through an order-book mechanism where traders buy and sell shares corresponding to different temperature ranges or outcomes. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each contract's price reflects the probability traders assign to that outcome occurring. As new information arrives—updated weather models, seasonal patterns, or real-time atmospheric data—traders adjust their bids and asks, moving prices up or down. The spread between buy and sell orders represents market uncertainty; tighter spreads indicate stronger consensus.

This market resolves around Jul 10, 2026, after July 9, 2026 concludes. The outcome is determined by the verified lowest temperature recorded in San Francisco that day, confirmed once credible public reporting becomes available. Traders' positions settle based on whether the actual low temperature matches their predicted outcome range. Resolution timing allows for official data collection and verification before final settlement.

Major weather pattern shifts, updated seasonal forecasts, and atmospheric pressure systems can significantly move this market. El Niño or La Niña conditions, jet stream positioning, and historical temperature anomalies for early July influence trader expectations. Real-time weather model updates—especially those issued in the weeks and days before July 9—often trigger sharp repricing. Additionally, unusual heat waves or cold snaps in preceding months may shift baseline assumptions about summer temperatures in the Bay Area, causing traders to adjust their positions accordingly.