TOTAL VOLUME:
$134b
24H VOL:
$103,397,351
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,410,176,180
399,592
Markets across
30,097
events
MATCHED EVENTS:
2,622
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Jun 3, 2:00 AM EST
Kalshi
The overnight low temperature in Austin on June 2, 2026, will be recorded by the National Weather Service according to their daily climatological report.
On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. On Kalshi, the lowest temperature in Austin on June 2, 2026 is priced as a set of binary or range-based contracts, each representing a specific temperature threshold or band. Traders buy or sell shares reflecting their belief in whether the actual low will fall within that range. Prices range from 0 to 100 cents per share, where higher prices indicate stronger market conviction that outcome will occur. Volume and bid-ask spreads on Kalshi reflect liquidity and trader interest in different temperature scenarios for that date.
The market resolves on Jun 3, 2026, after the close of June 2, 2026 in Austin. Resolution is determined by the official recorded lowest temperature for that calendar day at a designated weather station or data source. Once the actual low is published, the contract outcome is finalized and payouts are distributed to holders of the winning outcome. Traders should verify the exact data source and measurement methodology specified by Kalshi to understand which temperature reading will be used for settlement.
Major weather pattern shifts, including changes to high-pressure systems, cold fronts, or tropical moisture, can significantly alter Austin's overnight low on June 2. Long-range climate oscillations such as La Niña or El Niño influence seasonal temperature trends. Updated meteorological forecasts released in late May and early June will sharpen expectations as the date approaches. Urban heat island effects, cloud cover, and wind patterns on the day itself also play roles. Traders monitoring extended-range weather models and seasonal outlooks may adjust positions as new data emerges closer to resolution.