Lowest temperature in Washington DC on Jun 9, 2026?

Prediction market odds on Kalshi reflect aggregated trader expectations and often incorporate meteorological forecasts, historical June temperature data, and climate models. Comparing these odds to traditional weather analyst forecasts and seasonal normals can reveal whether the market is pricing in warmer or cooler conditions than official predictions suggest. Analysts typically issue deterministic point forecasts or narrow ranges, while prediction markets express uncertainty as probability distributions across multiple temperature outcomes. Divergences between the two often signal either market inefficiency or analyst confidence gaps worth investigating.

On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. On Kalshi, the lowest temperature in Washington DC on June 9 is priced as binary or range-based contracts reflecting different temperature thresholds. Traders buy and sell shares corresponding to whether the actual low will fall within specified bands—for example, below 60°F, 60–70°F, or above 70°F. The price of each contract reflects the collective probability assigned by the market. As new weather data emerges or the date approaches, prices adjust to reflect updated expectations. Liquidity and trading volume on each outcome determine how efficiently prices respond to new information.

The market resolves on Jun 10, 2026, after the lowest temperature for June 9, 2026, in Washington DC is recorded and verified. The outcome is determined by official temperature data from a designated weather station or meteorological authority serving the DC area. Once the actual low is confirmed, the contract settles to the outcome bracket or range that contains that temperature reading. Traders holding shares in the correct outcome receive their payout, while incorrect positions expire worthless. Resolution is typically finalized within hours of the market end date.

Major weather pattern shifts, including changes to jet stream positioning, high-pressure or low-pressure system development, and tropical activity, can significantly move odds. Updated seasonal forecasts from NOAA or the National Weather Service may alter trader expectations. Historical analogues—past June 9 temperatures and similar atmospheric setups—influence pricing. As June 9 approaches, deterministic forecast models become more reliable, typically narrowing the probability range. Urban heat island effects, cloud cover predictions, and wind patterns also factor into trader calculations. Real-time model runs in the days before the event often trigger sharp repricing.