Lowest temperature in Washington DC on Jun 13, 2026?

Prediction market odds reflect real-money incentives that often reveal trader conviction beyond traditional analyst consensus. While meteorologists and climate models provide deterministic forecasts, this market aggregates dispersed information from thousands of participants betting on specific temperature outcomes. Historical data shows prediction markets frequently outperform single-model or consensus forecasts when participants have access to diverse data sources and strong financial motivation. Comparing current odds to published seasonal outlooks and numerical weather prediction models can highlight where the market is pricing in tail risks or systematic biases that analysts may overlook.

On Kalshi, this market is priced through an order-book mechanism where traders submit bids and offers for each temperature outcome bracket. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Prices reflect the marginal cost of buying or selling a unit of exposure to a given outcome, with tighter spreads indicating higher confidence and liquidity. As new information arrives—updated seasonal forecasts, climate patterns, or real-time weather data—traders adjust their positions, moving prices to reflect evolving probabilities. The platform's matching engine ensures transparent price discovery and continuous repricing throughout the trading window.

This market resolves around Jun 14, 2026, after June 13, 2026 has passed and the lowest temperature recorded in Washington DC that day is confirmed. The outcome is determined by verifying the official low temperature against credible public sources, ensuring all traders have access to the same factual reference. Once the data is finalized and published, the market settles automatically, with payouts distributed to holders of the correct outcome bracket. Traders should monitor weather reporting from established meteorological authorities in the days leading up to and following the event date.

Major shifts in seasonal climate patterns, updated long-range forecasts, and anomalous atmospheric conditions can significantly move this market. El Niño or La Niña phase transitions, Arctic oscillation patterns, and jet stream positioning all influence June temperatures in the DC region. Real-time weather model updates in the week before June 13 typically trigger the largest repricing, as short-term forecasts become more precise. Additionally, any unusual heat waves or cold snaps earlier in spring may prompt traders to reassess their June expectations, creating trading opportunities as new data reshapes market odds.