On April 28, 2026, Mexico City will record a single daily high temperature, yet five distinct prediction markets below each propose a different precise outcome: exactly 19°C, 20°C, 21°C, 22°C, or 23°C. This structure creates a unique forecasting landscape—only one market can resolve true while the others settle false—making the complete set of odds an implicit probability distribution that should sum to approximately 100% if participants hold genuine forecasting confidence. By grouping these mutually exclusive possibilities, you can observe at a glance how the collective market allocates probability across each specific temperature threshold. As you read the prices below, notice which temperature threshold captures the highest implied probability; this represents where the prediction market expects the day's high to fall. Price clustering around a particular value reveals whether participants view the outcome as highly certain or genuinely open. The gradient of implied probabilities across thresholds also signals where forecast uncertainty concentrates—a steep drop-off between two adjacent prices suggests a boundary of confidence; a gradual slope suggests ambiguity about the most likely outcome. Because temperature is an objective, measurable outcome with no room for interpretation, these markets distill meteorological uncertainty into precise numerical forecasts. Whether you're interested in how crowds process weather data or curious about which temperature the prediction markets currently expect, this grouped view lets you see the full probability landscape at once.