Lowest temperature in San Francisco on Jun 26, 2026?

Prediction market odds reflect real-money bets from traders who profit when their forecasts prove accurate, creating a financial incentive for precision. Analyst forecasts, by contrast, are typically published estimates from meteorologists and climate services without direct financial stakes. This market aggregates distributed knowledge from many participants, often capturing nuances that single-source forecasts may miss. Over time, prediction markets have demonstrated competitive accuracy against traditional expert predictions, particularly when traders have access to the same underlying data. Comparing the odds here to published forecasts from the National Weather Service or similar agencies can reveal where market participants see divergence from consensus estimates.

On Kalshi, this market is priced through a continuous 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. The price of each outcome reflects the collective willingness of buyers and sellers to transact at that level, with tighter spreads indicating higher confidence and liquidity. As new weather models, seasonal data, or climate patterns emerge, traders adjust their positions, moving prices in real time. The platform's matching engine ensures transparent price discovery, so you see exactly what others are willing to pay or accept for exposure to each potential outcome.

This market resolves around Jun 27, 2026, after June 26, 2026 has passed and the lowest temperature for that day in San Francisco can be verified. The outcome is determined by comparing actual recorded temperatures against the predefined outcome categories established at market creation. Once credible public reporting confirms the final temperature reading, the market settles and winning positions are credited. Traders should monitor official weather station data and local meteorological records as the date approaches to anticipate how this market will likely resolve.

Major weather pattern shifts, updated seasonal forecasts, and long-range climate models can significantly influence trading activity and prices. El Niño or La Niña developments, atmospheric pressure systems, and ocean temperature anomalies all feed into meteorological predictions for June 2026. Real-time weather updates closer to the date will likely trigger sharp repricing as uncertainty narrows. Additionally, any revisions to historical temperature baselines or changes in how San Francisco's official weather station reports data could shift trader expectations. Geopolitical or industrial events affecting regional climate patterns may also prompt market participants to reassess their positions.