Lowest temperature in New York City on Jun 21, 2026?

Prediction market odds often diverge from traditional weather forecasts because they incorporate real-time trader sentiment and financial incentives alongside meteorological models. While the National Weather Service and private forecasters rely on physics-based atmospheric modeling, this market aggregates the collective judgment of participants betting actual capital on the outcome. Traders may weigh emerging climate patterns, historical anomalies, or long-range signals differently than conventional forecasters. Comparing the two reveals where market participants see value or risk that analysts may have underweighted, making both perspectives useful for understanding temperature expectations.

On Kalshi, this market is priced through a continuous order-book mechanism where buyers and sellers submit bids and offers for different temperature outcome ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Traders compete to set the odds by posting contracts at prices they believe reflect the true probability of each outcome. As new information arrives—updated weather models, seasonal data, or real-time atmospheric conditions—participants adjust their positions, which moves the market price. The spread between bid and ask prices reflects market liquidity and uncertainty around the exact low temperature on June 21.

This market resolves around Jun 22, 2026, once June 21 has passed and the day's low temperature in New York City can be verified. The outcome is confirmed against credible public sources that record official temperature data for the city. Once the actual low is recorded and published, the market settles based on which outcome range the verified temperature falls into. Traders holding positions in the correct outcome range receive their payout, while incorrect positions expire worthless.

Several factors could shift odds significantly before resolution. Updated seasonal climate forecasts, particularly from NOAA or international meteorological centers, often trigger repricing as traders adjust for new data. Major weather pattern shifts—such as early heat waves, cold fronts, or tropical systems affecting the Northeast—can rapidly change expectations. Historical temperature records and anomalies for late June in New York also influence positioning. As June 21 approaches, real-time weather models become more precise, typically narrowing uncertainty and stabilizing odds. Any unusual atmospheric conditions reported in the days leading up to the event will likely spark trading activity.