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Highest temperature in NYC on Jul 17, 2026?
kalshi

Highest temperature in NYC on Jul 17, 2026?

Volume:
$273,761

85° to 86°

 - Kalshi

85° to 86° - Kalshi

1W

News

Positive

Negative

Neutral

Hover marker for details

Vol.

·

Resolved Jul 18, 2026

Closed: Jul 18, 12:59 AM EST

kalshi

Kalshi

View
Join Kalshi and score $25 for your first trade.
Outcome
Trade
Chance %
Price
Spread
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
kalshi

85° to 86°

View
100%
Yes 100¢No 0¢
100¢
N/A
$92,314
N/A
N/A
$32,134
Settled
Yes
kalshi

87° to 88°

View
0%
Yes 0¢No 100¢
100¢
N/A
$96,262
N/A
N/A
$40,409
Settled
No
kalshi

83° to 84°

View
0%
Yes 0¢No 100¢
100¢
N/A
$35,432
N/A
N/A
$25,304
Settled
No
kalshi

89° to 90°

View
0%
Yes 0¢No 100¢
100¢
N/A
$20,746
N/A
N/A
$13,262
Settled
No
kalshi

82° or below

View
0%
Yes 0¢No 100¢
100¢
N/A
$18,111
N/A
N/A
$11,369
Settled
No
kalshi

91° or above

View
0%
Yes 0¢No 100¢
100¢
N/A
$10,896
N/A
N/A
$10,415
Settled
No
Total markets: 6

Description

This event tracks the highest temperature recorded in Central Park, New York on July 17, 2026. The outcome will be determined by the National Weather Service's official Climatological Report (Daily) for that date, with temperature ranges spanning from 82°F or below up to 91°F or above.

Kalshi

Resolution is based on the highest temperature recorded in Central Park, New York on July 17, 2026, as reported in the National Weather Service's Climatological Report (Daily). The temperature is divided into six distinct ranges: 82°F or below, 83–84°F, 85–86°F, 87–88°F, 89–90°F, and 91°F or above. Each range corresponds to a separate market outcome that resolves to Yes if the recorded temperature falls within that range. The NWS Climatological Report (Daily) serves as the sole authoritative source for resolution; preliminary NWS data and alternative weather services such as AccuWeather or Google Weather may be consulted for guidance but are not binding. Traders should be aware that preliminary NWS reporting and measurement methods may involve rounding and conversion nuances that could affect interpretation of data.