As May advances into early summer in Saudi Arabia, weather patterns in major cities like Jeddah shift notably, making short-term temperature forecasts both relevant and uncertain. This event aggregates five prediction markets all centered on a single focal point: Jeddah's highest temperature on May 17, 2026. Rather than a simple yes/no question, these linked markets represent overlapping temperature thresholds—27°C or below, 28°C, 29°C, 30°C, and 31°C—that collectively map the spectrum of plausible outcomes. This ensemble structure reveals something conventional weather forecasting often obscures: not just the single most likely temperature, but the full distribution of probabilities across a range. When you compare the odds on each bracket, patterns emerge. A sharp concentration of probability on one temperature (say, 30°C) signals strong consensus. A flatter distribution across multiple brackets indicates genuine uncertainty about which threshold will be crossed. The prices themselves reflect real-time synthesis of numerical weather models, historical climate data for Jeddah, atmospheric patterns across the Arabian Peninsula, and near-term meteorological updates. As May 17 approaches, new forecasts will circulate, and odds will shift to incorporate that information. This is prediction markets at their core: a mechanism that harnesses the distributed knowledge of thousands of participants to produce a continuously updated, transparent forecast. Unlike traditional weather services that issue a point estimate, prediction market prices embed the entire distribution of uncertainty, allowing you to see not only what forecasters expect but how confident they are. Whether you're researching climate prediction, exploring how crowds forecast real-world events, or simply curious about Jeddah's weather outlook, these linked markets offer an unfiltered, real-time window into probabilistic forecasting for a concrete, observable outcome.