TOTAL VOLUME:
$134.2b
24H VOL:
$134,145,987
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,441,166,947
406,422
Markets across
30,383
events
MATCHED EVENTS:
2,688
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: May 28, 4:00 AM EST
Kalshi
This event tracks the minimum temperature recorded in San Francisco on May 27, 2026, according to the National Weather Service's official climatological data.
On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. On Kalshi, the San Francisco May 27 low temperature is priced as binary or range-based contracts, with each outcome representing a specific temperature threshold or band. Traders buy and sell shares at prices between 0 and 100 cents, where the price reflects the market's implied probability of that outcome occurring. As new weather data, seasonal patterns, and real-time conditions emerge, prices adjust continuously. The market aggregates dispersed trader beliefs into a single price discovery mechanism for this localized weather event.
The market resolves on May 28, 2026, after May 27 has concluded and the lowest temperature recorded in San Francisco that day is finalized. Resolution is determined by official temperature data from a designated weather station or source specified in the contract terms. Once the actual low temperature is recorded and verified, the market settles automatically, paying out traders who held the correct outcome and returning losses to those on incorrect positions.
Several factors can shift odds for San Francisco's May 27 low temperature. Updated seasonal forecasts and long-range weather models showing cooler or warmer patterns will influence trader positioning. Real-time atmospheric conditions, including high-pressure systems, marine layer strength, and upper-level troughs, directly impact overnight lows. El Niño or La Niña phases affect late-May California weather. Historical May temperature data and anomalies also inform market expectations. As May approaches, short-range forecasts become more precise, typically tightening the range of plausible outcomes and sharpening price discovery on Kalshi.