| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Above 1 inch | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Above 2 inches | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Above 3 inches | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Above 4 inches | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Above 5 inches | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Above 6 inches | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Above 7 inches | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market tracks total precipitation levels in Miami, Florida, throughout the month of April 2026. It serves as a tool for understanding regional climate volatility and the precision of long-range meteorological modeling.
April marks the end of the dry season in South Florida, serving as a transition period before the onset of the summer wet season. While historically characterized by lower rainfall, shifting climate patterns and the influence of the El Niño-Southern Oscillation (ENSO) can lead to significant deviations from historical averages. Traders must account for how these seasonal transitions impact total monthly precipitation volume.
Market prices represent the collective expectation of future weather patterns, aggregating meteorological data and historical climate trends into a single indicator.
The market relies on official data from the National Weather Service (NWS) observation station located at Miami International Airport.
This market tracks the total cumulative depth of precipitation in inches, not the frequency of rainy days.
April is historically a drier month in Miami, but unexpected early season cold fronts or convective thunderstorms can cause significant spikes in total volume.
In the event of sensor failure or missing official records, the market will defer to the most reliable secondary data provided by the National Oceanic and Atmospheric Administration (NOAA).
While long-term climate trends provide context for warming, they do not guarantee specific precipitation outcomes for a single month, as localized weather patterns remain highly stochastic.