Lowest temperature in Washington DC on Jul 5, 2026?

Prediction market odds often diverge from traditional meteorological forecasts because they incorporate real-time trader behavior, risk premiums, and forward-looking sentiment rather than relying solely on numerical weather models. While meteorologists publish deterministic or probabilistic forecasts based on atmospheric data, this market aggregates the collective judgment of participants who have financial incentive to price outcomes accurately. Over time, prediction markets frequently outperform single-model forecasts, though both sources offer complementary perspectives on the likelihood of specific temperature outcomes.

On Kalshi, this market is priced through a continuous order-book mechanism where traders submit bids and asks for shares corresponding to different temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The price of each outcome reflects supply and demand; as more traders believe a particular range is likely, its share price rises, and vice versa. Settlement values are binary per outcome, meaning each temperature bracket either occurs or does not, and final payouts are determined once the actual low temperature is verified.

This market resolves around Jul 6, 2026, after July 5, 2026 has concluded and the day's lowest temperature is confirmed. The outcome is determined by the verified low temperature recorded for Washington DC on that date, checked against credible public sources. Once the actual low is established and matched to the corresponding outcome bracket in the market, traders holding shares in the correct range receive their payout, while other positions expire worthless.

Major weather pattern shifts, updated seasonal forecasts, and real-time atmospheric data releases will drive significant price swings in this market. Tropical systems, heat domes, or cold fronts affecting the Mid-Atlantic region in early July could substantially alter trader expectations. Additionally, historical temperature anomalies, climate indices like the North Atlantic Oscillation, and medium-range model consensus updates typically trigger repricing. As the event date approaches, short-term forecast refinements and any unusual weather alerts will likely produce the sharpest moves.