Lowest temperature in San Francisco on Jun 14, 2026?

Prediction market odds often diverge from traditional weather analyst forecasts because they incorporate real-time trading activity and diverse participant perspectives rather than relying on a single model or institution. While meteorologists publish deterministic forecasts based on atmospheric data and numerical weather prediction systems, this market aggregates the collective judgment of traders who may weight different climate signals, historical patterns, or uncertainty differently. Over time, prediction markets have demonstrated competitive accuracy against expert forecasts, particularly when participants have strong incentives and access to the same underlying information. Comparing the two approaches reveals how market-based probability estimates can complement or challenge conventional meteorological guidance.

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 reflects the market's implied probability, with prices ranging from near-zero to near-100 cents per share. Traders submit limit and market orders to express their views on the lowest temperature, and the bid-ask spread represents the current consensus and liquidity. As new information emerges—updated weather models, seasonal data, or real-time atmospheric conditions—prices adjust dynamically to reflect shifting expectations about the June 14 low.

This market resolves around Jun 15, 2026, once the lowest temperature recorded in San Francisco on June 14, 2026 is verified and confirmed. The outcome is determined by credible public reporting of the actual minimum temperature for that day, ensuring all participants have access to the same factual basis for settlement. Resolution typically occurs within hours of the market end date once official temperature data becomes available from established meteorological sources. Traders' positions are then settled based on which outcome range the verified temperature falls into.

Major weather pattern shifts, updated seasonal forecasts, and atmospheric anomalies can significantly move this market. El Niño or La Niña conditions, changes in Pacific Ocean temperatures, and shifts in the jet stream all influence June weather in San Francisco. Real-time weather model updates—particularly those issued in the days and weeks before June 14—often trigger sharp price movements as traders reassess probabilities. Historical temperature records, climate trends, and any unusual atmospheric events (volcanic activity, solar cycles) may also prompt traders to adjust positions. As the event date approaches, intraday weather forecasts and live atmospheric data become increasingly influential in driving market repricing.