| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| 78° or below | 1% | 0¢ | 1¢ | — | $17K | Trade → |
| 81° to 82° | 1% | 0¢ | 1¢ | — | $5K | Trade → |
| 79° to 80° | 99% | 99¢ | 100¢ | — | $4K | Trade → |
| 85° to 86° | 1% | 0¢ | 1¢ | — | $3K | Trade → |
| 83° to 84° | 1% | 0¢ | 1¢ | — | $3K | Trade → |
| 87° or above | 1% | 0¢ | 1¢ | — | $2K | Trade → |
This market asks which discrete temperature outcome will be the highest temperature recorded in San Antonio on March 7, 2026; it matters to traders who want to trade weather risk tied to short-term temperature outcomes.
San Antonio sits in a region where early March can swing between cool, seasonable conditions and unseasonably warm days due to the passage of Pacific and Gulf-origin weather systems. Short-term synoptic patterns, Gulf moisture, and occasional cold fronts drive most day-to-day variability, while long-term warming trends shift the distribution of extremes over decades.
Market prices reflect traders’ collective expectations about what the official highest-observed temperature will be on that date; consult the contract’s resolution rules to map the reported observation to the listed outcomes.
The event listing shows the close time as TBD; Kalshi will publish the specific trading cutoff on the market page — trading typically closes before the end of the observation day according to the contract rules.
The contract’s resolution section names the official observing station and data source (for example, an NWS station or airport METAR) that will be used to determine the highest recorded temperature; check the market page for that exact source and measurement conventions.
The market’s rules specify the observation window (usually the calendar day local time for the designated station) used to identify the highest temperature; refer to the contract text for the precise start and end times and time zone.
Short- to medium-range numerical model runs and ensemble forecasts drive traders’ expectations as the day approaches; model consensus, recent observation trends, and confidence in timing of fronts will tend to move market prices as new information arrives.
Historical climatology provides a baseline expectation and helps identify whether a given outcome would be unusually warm or cool, but traders should combine climatology with current forecast models and synoptic analysis because day-to-day weather variability can produce departures from typical values.