Lowest temperature in Boston on Jun 27, 2026?

Prediction market odds reflect crowdsourced expectations from traders betting real money, while meteorological forecasts rely on physics-based models and historical climate data. This market aggregates dispersed information from participants with financial incentives to predict accurately, often revealing different probability distributions than traditional weather models alone. Comparing the implied odds here to published seasonal temperature normals and near-term forecasts from the National Weather Service can highlight where traders see divergence from baseline expectations, signaling potential market inefficiencies or emerging consensus about unusual conditions.

On Kalshi, this market is priced through an order-book mechanism where traders submit bids and asks for outcome contracts tied to specific temperature ranges. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. Prices reflect the probability each outcome attracts, with tighter spreads indicating higher confidence and liquidity. As new information emerges—updated forecasts, seasonal shifts, or real-time weather data—traders adjust their positions, moving prices to equilibrate supply and demand across all temperature brackets.

This market resolves around Jun 28, 2026, once June 27, 2026 has passed and the actual low temperature in Boston is verifiable from credible public sources. The outcome is determined by comparing recorded temperature data against the predefined ranges offered in the market, with winning contracts paid out based on which bracket the official low falls into. Resolution occurs after sufficient time for official meteorological records to be finalized and confirmed.

Major shifts in seasonal climate patterns, updated long-range forecasts, and historical anomalies in late June temperatures could significantly influence trader positioning. El Niño or La Niña conditions, Arctic oscillation patterns, and jet stream behavior in early summer all carry predictive weight. Additionally, any unusual atmospheric events—volcanic activity, solar cycles, or regional heat waves—may prompt traders to reassess probabilities. As June 27 approaches, near-term weather models will dominate price discovery, with daily forecast updates potentially triggering sharp moves in the odds.