Lowest temperature in San Francisco on Jun 16, 2026?

Prediction market odds reflect real-money incentives for accuracy, often incorporating meteorological forecasts, historical climate patterns, and seasonal trends in ways that differ from traditional analyst models. While weather forecasters rely on atmospheric data and numerical models, this market aggregates dispersed information from traders who face direct financial consequences for incorrect predictions. Over longer horizons like June 2026, prediction markets can capture uncertainty and tail risks that point forecasts may underweight. Comparing the two approaches reveals how market-based probability estimates diverge from or align with expert consensus on temperature outcomes.

On Kalshi, this market is priced through a continuous order book 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 trades at a price between 0 and 100 cents, with that price reflecting the market's implied probability. Traders profit by correctly predicting the lowest temperature and can enter or exit positions at any time before resolution. The spread between bid and ask prices tightens as liquidity increases, and volume concentration signals where the market consensus is strongest.

This market resolves around Jun 17, 2026, once the lowest temperature recorded in San Francisco on June 16, 2026 becomes verifiable from credible public sources. The outcome is determined by comparing actual meteorological data against the temperature ranges defined in the market's outcome options. Resolution occurs after the event date passes and official temperature readings are confirmed, ensuring all traders have equal access to the same factual basis for settlement.

Major climate patterns, seasonal forecasts, and long-range weather models will influence trader expectations as June 2026 approaches. El Niño or La Niña conditions, shifts in the Pacific high-pressure system, and anomalies in sea-surface temperatures could all shift the probability distribution. Closer to the event date, standard meteorological forecasts will become more precise and may trigger significant repricing. Historical temperature records for mid-June in San Francisco and any unusual atmospheric developments will also guide market participants' views on the likelihood of extreme outcomes.