Lowest temperature in Minneapolis on Jun 28, 2026?

Prediction market odds often diverge from traditional meteorological forecasts because they reflect real-money stakes and crowd sentiment rather than deterministic models alone. While weather analysts rely on atmospheric data and historical patterns, traders in this market incorporate broader uncertainty and tail-risk pricing. Over time, prediction markets have proven competitive with or superior to single-model forecasts for binary outcomes, though they excel at capturing consensus probability rather than point estimates. Comparing this market's implied odds to National Weather Service long-range forecasts for late June in Minneapolis can reveal whether traders are pricing in above-average or below-average temperature risk.

On Kalshi, this market is priced through an order-book mechanism where traders buy and sell shares corresponding to different temperature outcome ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Each outcome contract trades independently, and the current bid-ask spread reflects the market's confidence in that range. Prices range from 0 to 100 cents per share, with the sum of all outcome probabilities typically near 100 due to the platform's design. As new information arrives—seasonal forecasts, climate patterns, or real-time weather updates—traders adjust their positions, moving prices to reflect updated consensus.

This market resolves around Jun 29, 2026, once the lowest temperature recorded in Minneapolis on June 28, 2026 is verified against credible public sources. The outcome is determined by official meteorological data, typically from the National Weather Service or equivalent authoritative weather reporting. Traders' positions settle based on which temperature range the actual low falls into, with payouts distributed accordingly. Resolution occurs shortly after the event date, pending confirmation of the final temperature reading.

Several factors could shift odds in this market before resolution. Long-range climate forecasts and seasonal outlooks released by meteorological agencies often trigger repricing as traders adjust for broader atmospheric patterns. El Niño or La Niña conditions, Arctic oscillation shifts, and historical temperature anomalies for late June in Minneapolis all influence expectations. Closer to the event, weekly weather models and real-time atmospheric data become more predictive, typically causing sharper price movements. Unusual heat waves or cold snaps in the weeks prior may also signal regime changes that traders incorporate into their positions.