Lowest temperature in Austin on Jun 28, 2026?

Prediction market odds often diverge from traditional weather analyst forecasts because they incorporate real-time trader conviction and aggregate dispersed information across many participants. While meteorologists issue point forecasts and confidence intervals based on models, this market reflects what thousands of traders believe will actually occur, factoring in uncertainty and tail risks. Over time, prediction markets have proven competitive with or superior to expert forecasts in many domains, though both sources offer complementary perspectives on the likely low temperature outcome.

On Kalshi, this market is priced through a continuous order book where buyers and sellers submit bids and asks for different temperature ranges or specific outcomes. On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. The market price reflects the marginal trade—the last executed transaction—and moves as new information arrives or trader sentiment shifts. Liquidity and trading volume determine how easily you can enter or exit positions, and wider spreads typically appear during low-activity periods or when uncertainty is highest.

This market resolves around Jun 29, 2026, after June 28, 2026 has passed and the lowest temperature recorded in Austin that day is verified. The outcome is confirmed once credible public reporting—such as data from the National Weather Service or established meteorological records—becomes available. All open positions settle based on the verified low temperature, and traders' profits or losses are finalized according to their position and the final outcome.

Major weather pattern shifts, seasonal climate anomalies, and updated long-range forecasts from meteorological agencies can all move this market significantly. El Niño or La Niña developments, unusual jet stream positioning, and historical temperature comparisons for late June in Austin will influence trader positioning. As the event date approaches, short-range weather models become more reliable, typically triggering sharper price moves. Breaking news about atmospheric conditions or unexpected climate events in the weeks prior could also drive rapid repricing of temperature expectations.