Lowest temperature in San Francisco on Jul 1, 2026?

Prediction market odds often diverge from traditional meteorological forecasts because they reflect real-money incentives and live trader sentiment rather than deterministic weather models. While meteorologists issue point estimates or confidence intervals based on atmospheric data, this market prices uncertainty through continuous trading. Traders incorporate not only official forecasts but also historical temperature patterns, climate trends, and emerging weather signals. Over time, prediction markets have demonstrated competitive accuracy against expert consensus, particularly when liquidity is robust and participants have strong incentives to forecast accurately.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares corresponding to different temperature outcome ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome contract trades independently, and the price of a share reflects the collective probability assigned by the market. Traders profit by correctly predicting the lowest temperature, with payouts determined by which range the actual reading falls into. Spreads between bid and ask prices tighten as more volume flows through the market, rewarding early forecasters and allowing late arrivals to trade at more efficient prices.

This market resolves around Jul 2, 2026, once the lowest temperature recorded in San Francisco on July 1, 2026 is verified and finalized. The outcome is determined by the actual observed low temperature for that calendar day, confirmed through credible public weather reporting. Traders' positions settle based on which outcome range the verified temperature falls into. Resolution typically occurs within hours of the market close, allowing winners to collect payouts and losers to understand the final result.

Several factors can shift odds in this market over the coming months. Seasonal climate patterns and long-range forecasts issued by meteorological agencies often trigger repricing as new data emerges. El Niño or La Niña conditions, atmospheric pressure systems, and historical temperature anomalies for early July in San Francisco all influence trader positioning. As the event date approaches, updated 10-day and extended forecasts become more reliable, typically causing sharper price movements. Major weather events or climate announcements in the preceding weeks can also prompt rapid adjustments as participants reassess their probability estimates.