This market will resolve to the temperature range that contains the highest temperature recorded by NOAA at the Minister Pistarini Intl Airport Station in degrees Celsius on 3 Sep '26. The resolution source for this market will be information from NOAA, specifically the highest reading under the "Temp" column for all times on this day, available here: https://www.weather.gov/wrh/timeseries?site=saez If NOAA data for the observation date is unavailable by 11:59 PM ET on the day following the observation date, the Weather Underground Daily Observations table will be used as the resolution source. In the event that there is no data for the observation date by 11:59 PM ET on the day following the observation date, this market will resolve to the lowest bracket. To toggle between Fahrenheit and Celsius, click the "Switch to Metric Units" button until the relevant table displays °C. This market will resolve once the first data point for the following date has been published on the resolution source, or by 11:59 PM ET on the day following the observation date, whichever comes first. The resolution source for this market measures temperatures to whole degrees Celsius (eg, 9°C). Thus, this is the level of precision that will be used when resolving the market. Revisions to temperatures recorded within this market's timeframe will be considered until the first datapoint for the following date has been published, after which any alterations will not be considered.
Trader consensus has settled decisively on 18°C as the highest temperature recorded in Buenos Aires on September 3, 2026. Official observations from the Servicio Meteorológico Nacional aligned with pre-event forecasts calling for a maximum of 18°C under partly cloudy skies, moderate southwest winds around 20 km/h, and humidity near 80%. This outcome reflects typical early-spring conditions for the region, where daily highs often range 16–19°C with limited diurnal variation once frontal systems stabilize. The market-implied probability near 100% indicates traders viewed the forecast consensus and station data as highly reliable, with minimal room for revision. Only an unexpected measurement discrepancy at the resolution station or a late data correction could realistically alter the result.