Lowest temperature in San Francisco on Jun 18, 2026?

Prediction market odds often diverge from traditional meteorologist forecasts because traders incorporate real-time data, historical patterns, and their own risk assessments into prices. While weather analysts rely on numerical models and satellite imagery to project conditions months ahead, this market aggregates the collective judgment of many participants betting their capital on the outcome. Comparing the implied probability here to published seasonal forecasts can reveal where the crowd sees value or risk that official models may underweight, especially given the long lead time to mid-June 2026.

On Kalshi, this market is priced through a continuous order-book mechanism where traders buy and sell shares corresponding to different temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome contract reflects the probability traders assign to that temperature band occurring on June 18, 2026. Prices move in real time as new bids and offers arrive, so the odds you see represent the marginal consensus at that moment. Tighter spreads between buy and sell prices typically indicate higher confidence or liquidity around particular temperature thresholds.

This market resolves around Jun 19, 2026, once June 18, 2026 has passed and the lowest temperature for that day is verifiable from credible public sources. The outcome is determined by comparing actual recorded low temperatures against the contract specifications, with winning shares paid out based on which temperature band the official reading falls into. Resolution typically occurs within hours or a day after the event, once data is confirmed and published by recognized meteorological authorities.

Major climate patterns, seasonal forecasts, and El Niño or La Niña developments can shift odds significantly over the coming months. As spring 2026 approaches, updated long-range weather models will refine expectations for June conditions in the Bay Area. Unusual heat waves or cold snaps in preceding months may also influence trader sentiment about whether June will follow historical norms. Real-time atmospheric data and any unexpected weather events in the region will likely trigger repricing as new information becomes available.