Will the highest temperature in Mexico City be 22°C on May 12?

Market participants currently imply a 21.0% probability for "Will the highest temperature in Mexico City be 22°C on May 12?".

The YES side is priced at 21.0¢, and the NO side at 64.0¢.

Liquidity is low, supported by $1,819 in recent trading activity.

Resolution Criteria

This market will resolve to the temperature range that contains the highest temperature recorded at the Benito Juárez International Airport Station in degrees Celsius on 12 May '26.

The resolution source for this market will be information from Wunderground, specifically the highest temperature recorded for all times on this day by the Forecast for the Benito Juárez International Airport Station once information is finalized, available here: https://www.wunderground.com/history/daily/mx/mexico-city/MMMX.

To toggle between Fahrenheit and Celsius, click the gear icon next to the search bar and switch the Temperature setting between °F and °C.

This market can not resolve to "Yes" until all data for this date has been finalized.

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.

Any revisions to temperatures recorded after data is finalized for this market's timeframe will not be considered for this market's resolution.

Market Interpretation

Prediction markets function as real-time consensus engines.

Traders continuously buy and sell outcome shares based on:

• breaking news
• macro developments
• public narratives
• institutional positioning
• probability reassessments

As a result, market pricing reflects aggregate trader expectations rather than static forecasts or polling systems.

At the current pricing structure:

• YES trades near 21.0¢
• NO trades near 64.0¢
• Implied probability sits near 21.0%

These probabilities may shift rapidly as new information enters the market.

Liquidity & Conviction Analysis

As of May 12, 2026 at 09:29 AM, liquidity conditions act as a primary structural filter on prediction market signal quality.

Medium liquidity conviction suggests moderate participation depth, where price discovery is active but not fully saturated by institutional or high-frequency flow.

Higher liquidity environments typically produce:

• tighter spreads
• faster price discovery
• stronger informational efficiency
• lower pricing instability

Lower liquidity environments tend to exhibit:

• wider spreads
• delayed consensus formation
• increased volatility from isolated trades
• weaker signal reliability in short time windows

Overall, liquidity acts as a direct proxy for how “stable” the implied probability surface is at any given moment.

Why This Signal Exists in Prediction Markets

Prediction markets function as continuous consensus engines where probability is not stated — it is priced.

Each trade updates a live belief distribution, turning scattered human judgment into a single evolving likelihood curve.

Compared to static polling or narrative reporting, this structure adapts instantly to:

• regime shifts in geopolitics
• macroeconomic shocks and policy changes
• institutional order flow and positioning
• narrative acceleration or decay
• liquidity-driven sentiment swings
• information asymmetry correction

In practice, these markets behave less like betting tools and more like real-time probabilistic sensors for world events.

They compress collective intelligence into a dynamic signal that updates with every transaction.

Market Structure Transition

As of May 12, 2026 at 09:29 AM, prediction markets have evolved into persistent global probability infrastructure.

Polymarket and Kalshi now operate as high-throughput probability engines, with cumulative volumes exceeding $150B+ and sustained monthly flow above $7B.

Market activity has shifted from episodic speculation toward continuous liquidity formation, where geopolitical events, macroeconomic narratives, elections, AI milestones, and financial expectations are constantly repriced in real time.

This transformation has turned prediction markets into always-on consensus surfaces capable of reflecting crowd intelligence faster than traditional media, polling systems, or institutional forecasting pipelines.