Lowest temperature in Los Angeles on Jun 13, 2026?

Prediction market odds often diverge from traditional meteorological forecasts because they incorporate real-time trader behavior, risk appetite, and information asymmetries. While weather analysts rely on physics-based models and historical data, this market aggregates the collective judgment of participants with financial incentives to forecast accurately. Prediction markets can sometimes lead or lag official forecasts depending on how quickly new data is priced in. Comparing the implied probability from market odds to the confidence intervals from major weather services provides insight into where consensus differs and where uncertainty concentrates.

On Kalshi, this market is priced through a continuous order-book mechanism where traders submit bids and asks for shares tied to specific 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 marginal probability assigned by the last matched trade, and spreads between bid and ask widen or narrow based on liquidity and conviction. As new information arrives—updated weather models, seasonal anomalies, or real-time atmospheric data—traders adjust their positions, moving prices to reflect revised expectations for the lowest temperature on that date.

This market resolves around Jun 14, 2026, once the event date has passed and the lowest temperature for Los Angeles on June 13, 2026 can be verified from credible public sources. The outcome is determined by comparing actual recorded temperatures against the prediction brackets or thresholds defined in the market. Resolution occurs after sufficient time has elapsed for official meteorological data to be published and confirmed. Traders holding shares in the correct outcome receive their payout once the market settles.

Several factors can shift prices in this market: updated long-range weather forecasts from the National Weather Service or international models, El Niño or La Niña phase changes affecting Pacific weather patterns, unusual atmospheric circulation anomalies, and seasonal climate reports. Real-time developments closer to June 2026—such as heat waves, cold fronts, or volcanic activity affecting solar radiation—will drive sharp repricing. Media coverage of extreme weather events or climate records can also influence trader sentiment. As the event date approaches, intraday temperature swings and final model consensus will likely create the most volatile trading activity.