Most people think prediction markets are for politics and sports. They are missing one of the most consistently profitable and data-driven categories on the entire platform.
Polymarket currently hosts over 463 active weather markets covering daily high temperatures across global cities, hurricane formations, tornado events, precipitation totals, and long-range climate outcomes. The combined climate and weather category holds over $16.5 million in cumulative trading volume. Single daily temperature markets for major cities regularly clear $300,000 to $400,000 in 24-hour volume.
The reason weather markets attract serious traders is structural. Weather outcomes are determined by objective, publicly available data from sources like NOAA and the National Weather Service. Professional weather models update on fixed six-hour schedules. Market prices do not always reflect those updates in real time. That gap between what the science says and what the market prices imply is where the edge lives.
This guide covers everything you need to know to start trading weather markets on Polymarket, from understanding how markets are structured to reading the right data sources to applying the strategies that top traders have used to generate consistent returns.
What Are Polymarket Weather Markets?
Polymarket weather markets are prediction market contracts that resolve based on real-world meteorological outcomes. Each contract poses a specific, measurable question about a future weather event, and the market price reflects the collective probability that traders assign to each outcome.
How they are structured
Weather markets on Polymarket come in two primary formats.
Binary YES/NO markets: These ask a simple true-or-false question. Examples include:
- "Will a named storm form before June 1, 2026?"
- "Will a Category 4 hurricane make landfall in the US before 2027?"
- "Major solar storm by April 30?"
You buy YES shares if you believe the event will happen, or NO shares if you believe it will not. If YES is priced at $0.30, the market implies a 30% probability. If the event occurs, YES shares pay $1.00 each. If it does not, they pay $0.00.
Multi-outcome temperature range markets: These are the most heavily traded weather markets on Polymarket. A typical example looks like this:
"Highest temperature in New York City on April 15?"
The market presents multiple outcome buckets, for example:
- Below 50°F — trading at $0.05
- 50°F to 54°F — trading at $0.12
- 55°F to 59°F — trading at $0.35
- 60°F to 64°F — trading at $0.30
- 65°F or above — trading at $0.18
You buy shares in the specific temperature range you believe will occur. Each outcome is priced between $0.01 and $0.99, and the winning outcome pays $1.00 at resolution.
What cities and events are covered
As of April 2026, Polymarket weather markets cover:
Daily temperature markets (most active):
- New York City (LaGuardia station data)
- Los Angeles (LAX or downtown station data)
- London (London City Airport, station code EGLC)
- Seoul
- Shanghai
- Hong Kong
- Chicago
- Miami
- Tokyo
- Sydney, and dozens of others
Event markets:
- Atlantic hurricane formation and landfall
- Named storm activity
- Tornado events
- Precipitation totals
- Solar weather events
- Global temperature ranking markets
Climate and long-range markets:
- Where will 2026 rank among the hottest years on record?
- Will a Category 4 hurricane make a US landfall before 2027?
- Natural disaster occurrence markets
Suggested image: Screenshot of Polymarket's weather category page showing multiple active temperature markets for different cities with their current prices and volumes.
Why Weather Markets Have a Tradeable Edge
The core insight behind profitable weather trading on Polymarket is not meteorological expertise. It is information asymmetry between what professional forecasting models know and what the market price currently reflects.
The mechanics of the edge
Professional weather models, primarily the Global Forecast System (GFS) operated by NOAA and the European Centre for Medium-Range Weather Forecasts (ECMWF) model, update on fixed schedules approximately every six hours. These models are the result of decades of scientific development, satellite data, ground station readings, and computational modeling.
Polymarket prices, by contrast, are set by individual traders who range from sophisticated systematic traders to casual participants making gut-feel bets. When a new model updates a temperature forecast significantly, the Polymarket price for the affected market does not always update at the same speed.
That window, between when the science updates and when the market prices, is the primary source of edge in weather trading.
A concrete example
At 12:00 UTC, the GFS model updates. The new run shows that cold air is moving into New York City faster than previously expected, shifting the forecast high temperature from 62°F to 54°F for tomorrow.
Before this update, the market had the "55°F to 59°F" bucket priced at $0.18. After a model updates this significantly, the correct price for that bucket should be closer to $0.55 or higher. But not all traders have seen the new model run yet. The price is still at $0.18.
You buy shares in the "55°F to 59°F" bucket at $0.18 per share before the market prices. Other traders see the updated forecast, begin buying the same bucket, and the price rises to $0.50. You can either sell at $0.50 for a significant profit before resolution, or hold to resolution and collect $1.00 per share if the temperature falls in that range.
The documented performance
One documented trader known as gopfan2 reportedly generated over $2 million in net profit on Polymarket, with the majority coming from weather markets specifically. His strategy is described as buying YES shares priced below $0.15 and buying NO shares priced above $0.45, limiting risk to approximately $1 per position, and repeating this across thousands of trades.
Another documented trader, meropi, generated roughly $30,000 in profit using fully automated $1 to $3 micro-bets, sometimes entering positions at $0.01 per share which produced payoffs of up to 500 times the initial stake when long-shot outcomes hit.
A third trader called 1pixel documented approximately $18,500 in profit from just $2,300 in total deposits, trading only NYC and London weather markets, with individual trades turning $6 into $590 or $15 into $547 when he identified mispriced ranges.
The Resolution Sources You Must Know
Before placing any weather trade on Polymarket, you must understand exactly which data source will determine whether your trade wins or loses. This is non-negotiable.
Every Polymarket weather market specifies its resolution source in the Rules section of the individual market page. This source is fixed at market creation and does not change.
Most common resolution sources
NOAA official station records: The most common resolution source for US temperature markets. NOAA maintains thousands of weather stations across the US and provides official readings that are definitive and publicly accessible. The specific station referenced in the market rules is what matters, not the forecast.
National Weather Service (NWS): Used for US-based event markets such as tornado declarations and precipitation measurements. The NWS provides official designations for named weather events and storm classifications.
National Hurricane Center (NHC): Used for all Atlantic hurricane and tropical storm markets. The NHC's official storm classification is the definitive source for any market involving hurricane formation, intensification, or landfall.
Weather Underground: Used for some international temperature markets, particularly London markets. The specific weather station code is important. For London temperature markets, the resolution often comes from the London City Airport station, identified by the code EGLC.
AccuWeather verified data: Used for some markets as a secondary verification source.
How to find the resolution source for any market
- Go to the specific market page on Polymarket
- Click the Rules or Details tab below the market question
- Read the resolution criteria carefully
- Note the specific data source named
- Verify that the data source you plan to use for your analysis matches the resolution source
This step eliminates one of the most common mistakes new weather traders make, which is researching a different weather station or data source than the one used for resolution.
The Primary Data Sources for Weather Market Analysis
Once you know the resolution source, you need to access the actual forecast data that gives you an edge over market prices.
NOAA and the National Weather Service
What it provides: Official hourly and daily forecasts for thousands of US locations. The NWS Forecast Discussion document, updated multiple times per day, explains the reasoning behind the forecast in detail and flags areas of uncertainty.
How to access it:
- weather.gov: Official NWS forecasts for all US locations
- NOAA Climate Data Online: Historical records for specific stations
- NOAA Global Forecast System API: Raw GFS model data
Why it matters: For any US-based temperature or precipitation market, the NWS forecast for the specific station referenced in the market rules is your primary analytical tool.
ECMWF (European Centre for Medium-Range Weather Forecasts)
What it provides: Widely considered the most accurate global weather model, particularly for 3 to 10 day forecasts. The ECMWF model updates twice daily and provides ensemble forecasts that quantify uncertainty.
Free access: ECMWF data is accessible through Open-Meteo, a free weather API that aggregates data from multiple models including ECMWF, GFS, UKMO, and others.
Open-Meteo
Open-Meteo is a free, open-source weather API that provides access to GFS, ECMWF, UKMO, and NWS forecast data in a single API call. It is the most practical data source for systematic weather trading without paying for premium data subscriptions.
import requests
def get_temperature_forecast(lat, lon, target_date):
url = "https://api.open-meteo.com/v1/forecast"
params = {
"latitude": lat,
"longitude": lon,
"daily": "temperature_2m_max",
"temperature_unit": "fahrenheit",
"forecast_days": 14,
"models": "gfs_seamless"
}
response = requests.get(url, params=params)
data = response.json()
dates = data['daily']['time']
temps = data['daily']['temperature_2m_max']
for date, temp in zip(dates, temps):
if date == target_date:
return temp
return None
Weather Underground
Weather Underground provides historical and near-real-time data from thousands of personal weather stations and official stations worldwide. For markets that resolve against Weather Underground data, such as London temperature markets using the EGLC station code, this is the source to monitor.
Step-by-Step: How to Place Your First Weather Trade
Step 1: Create and fund a Polymarket account
Polymarket accepts funding via:
- Crypto (USDC on Polygon network, directly to your wallet)
- Credit or debit card
- Bank transfer
For new traders, start with a small amount you are comfortable learning with. $20 to $50 in USDC is sufficient to practice the mechanics of placing orders.
Step 2: Navigate to the weather markets
From the Polymarket homepage:
- Click Explore or browse by category
- Select Weather or Climate & Weather from the category menu
- You will see all active weather markets sorted by volume
The URL shortcuts for weather subcategories are:
- polymarket.com/predictions/weather
- polymarket.com/predictions/temperature
- polymarket.com/predictions/climate-weather
- polymarket.com/predictions/forecast
Step 3: Select a market and read the rules
Click any market to open its detail page. Before doing anything else:
- Read the full question carefully
- Click the Rules or Details tab
- Note the exact resolution source (NOAA station, NWS, NHC, Weather Underground)
- Note the resolution date and time
- Understand what specific measurement will determine the outcome
For a temperature range market, the rules will specify which weather station's reading will be used and which temperature metric (typically the official daily high) determines resolution.
Step 4: Check the current forecast
Using the resolution source you identified in Step 3, look up the current forecast for the date in question.
For a US temperature market: Go to weather.gov and enter the city. Look at the official forecast for the target date. Note the high temperature forecast and the confidence range.
For a London temperature market: Check Weather Underground for the EGLC station. Note the current forecast high.
For a hurricane market: Check nhc.noaa.gov for the current official storm tracking and forecast.
Step 5: Compare the forecast to market prices
Now look at the market's current outcome prices. Each outcome bucket is priced between $0 and $1.
Calculate whether the market price matches the forecast:
- If NOAA forecasts 62°F for NYC tomorrow and the "60°F to 64°F" bucket is priced at $0.25, the market is implying only a 25% probability for the range that the official forecast is pointing directly at. This is a significant discrepancy.
- If the official forecast shows 62°F and the "60°F to 64°F" bucket is at $0.65, the market has already priced in the forecast accurately. There is less edge here.
Step 6: Place your order
Select the outcome bucket you want to trade. Click Buy. Enter your position size in USDC. Review the price per share. Confirm the order.
For new traders, use the market order option for simplicity. Once you understand the platform, limit orders allow you to specify the exact price you are willing to pay.
Step 7: Monitor and manage your position
After placing the trade, you can:
- Hold to resolution and collect $1.00 per share if your outcome wins
- Sell before resolution if the price has moved in your favor and you want to lock in a profit
- Sell before resolution to cut a loss if the forecast changes significantly
The Four Core Strategies for Weather Market Trading
Strategy 1: Forecast vs. Market Price Divergence
This is the Polymarket foundational strategy for all weather market trading. It involves identifying markets where the current professional forecast clearly supports one outcome but the market price has not yet reflected that information.
The process:
- Scan active temperature markets for your preferred cities
- Check the current professional forecast for each market's target date
- Identify markets where the forecast-implied probability differs significantly from the current price
- Buy the underpriced outcome bucket
The key principle: You are not predicting the weather. You are identifying cases where the market's crowd-sourced probability is out of line with what professional meteorological models are saying. Science provides the edge. The market price provides the opportunity.
Practical threshold: A divergence of more than 10 to 15 percentage points between the forecast-implied probability and the market price is generally considered the minimum threshold worth trading. Smaller divergences may not produce reliable returns after accounting for position costs.
Strategy 2: Model Update Timing (The Six-Hour Window)
GFS and ECMWF models update on fixed schedules. GFS updates at 00:00, 06:00, 12:00, and 18:00 UTC. ECMWF updates at 00:00 and 12:00 UTC.
When a new model run is released, it can shift temperature forecasts meaningfully for the coming days. Polymarket prices do not always update simultaneously.
The approach: Monitor model update times. Immediately after each major model run release, check whether the new forecast has shifted significantly from the previous run. If it has, scan Polymarket weather markets for the affected dates and cities to find prices that have not yet repriced to the new forecast.
This strategy requires more active monitoring but can produce the clearest and largest edges because you are acting on new information before the broader market has processed it.
Strategy 3: The Low-Price Range Strategy (gopfan2 Approach)
One of the most documented weather trading strategies is based on the observation that very low-priced outcome buckets (those priced below $0.10 to $0.15) are frequently underpriced, and very high-priced outcome buckets (those priced above $0.50 to $0.60) are frequently overpriced.
The logic is that market participants tend to overestimate the probability of the most likely outcome and underestimate the probability of adjacent ranges. A temperature forecast of 62°F might lead traders to assign 70% probability to the "60°F to 64°F" bucket, leaving adjacent buckets like "55°F to 59°F" severely underpriced even though there is meaningful probability of the temperature falling slightly below the point forecast.
The mechanical implementation:
- Buy outcome buckets priced below $0.15 when the NOAA forecast does not rule them out
- Consider selling positions in buckets priced above $0.45 that the forecast does not strongly support
- Limit individual position size to $1 to $5 per trade to manage risk across many positions
This strategy is particularly effective when applied systematically across many markets simultaneously rather than concentrating large positions in individual markets.
Strategy 4: Near-Resolution Certainty Trading
In the 6 to 24 hours before a temperature market resolves, actual weather station readings begin to confirm the day's temperature direction. At this stage, the uncertainty in the forecast collapses significantly.
If a market resolves at midnight based on the official daily high, and by late afternoon the temperature station has already recorded a high of 67°F with no significant change expected before market close, the "65°F to 69°F" bucket should be very close to $1.00. If it is still trading at $0.70 to $0.80, there is a near-certain return available by buying the winning bucket.
This strategy requires monitoring markets in the hours before resolution and comparing real-time station readings against market prices. The returns per dollar are smaller because prices are already elevated, but the risk is significantly lower than earlier-stage trades.
Types of Weather Markets and How to Approach Each
Daily temperature markets
Frequency: New markets open daily for many cities Resolution time: Typically at midnight local time or end of the meteorological day Best data source: NWS for US cities, Weather Underground for European cities Key skill: Reading the point forecast and estimating uncertainty around it Time horizon: Same-day or next-day trades
These are the most active and most liquid weather markets. They resolve quickly and provide frequent trading opportunities. The short time horizon means forecasts are highly reliable, making edge identification more straightforward.
Hurricane and storm formation markets
Frequency: Seasonal, primarily June through November for Atlantic storms Resolution time: Based on NHC official classifications Best data source: nhc.noaa.gov, tropical weather outlooks Key skill: Understanding tropical weather development probability Time horizon: Days to weeks
These markets attract more sophisticated traders during hurricane season and tend to be well-traded with tighter pricing. The NHC publishes Tropical Weather Outlooks that assign explicit probability percentages to potential storm development, making these markets highly data-driven.
Global temperature ranking markets
Frequency: Monthly and annual Resolution time: When official global temperature data is published Best data source: NOAA Global Surface Temperature dataset, NASA GISS Key skill: Understanding climate baseline data and anomalies Time horizon: Weeks to months
These are the longer-duration weather markets. The "Where will 2026 rank among the hottest years on record?" market is one of the most actively traded climate markets on the platform, with the crowd currently assigning the highest probability to second place.
Space weather markets
Frequency: Ongoing based on solar activity Resolution time: Based on official NOAA Space Weather Prediction Center classifications Best data source: NOAA Space Weather Prediction Center (spaceweather.noaa.gov) Key skill: Understanding the Kp index and geomagnetic storm classification Time horizon: Hours to days
Space weather markets cover events such as major geomagnetic storms classified by the Kp index. While smaller in volume than temperature markets, they offer opportunities for traders who follow space weather data.
Reading a Weather Market Correctly: A Worked Example
Let us walk through a complete trade analysis for a hypothetical NYC temperature market.
The market: "Highest temperature in New York City on April 20?"
The prices sum to 100%, which is correct.
Step 1: Check the resolution source The rules state: resolution uses NOAA ASOS station at LaGuardia Airport (KLGA).
Step 2: Check the current NWS forecast The NWS forecast for Central Park (close to LaGuardia) shows: High of 63°F, with a range of 61°F to 65°F considered likely based on the forecast discussion.
Step 3: Assess the market prices against the forecast
The forecast is strongly pointing toward the "60°F to 64°F" bucket, which is priced at $0.38. That is reasonable and does not represent a major mispricing.
However, the forecast range of 61°F to 65°F means there is meaningful probability in the "65°F to 69°F" bucket as well, but that bucket is only priced at $0.20. Given that the forecast's upper range extends into this bucket, the true probability is likely higher than 20%.
The "55°F to 59°F" bucket at $0.22 is within the lower end of the forecast range, meaning there is also real probability there.
Step 4: Identify the trade
The "65°F to 69°F" bucket at $0.20 appears underpriced relative to the forecast uncertainty. If the forecast's upper range at 65°F has a roughly 30% to 35% probability of occurring based on typical forecast uncertainty at this time range, the fair price for that bucket is higher than the current $0.20.
Trade: Buy the "65°F to 69°F" bucket at $0.20 for $10.
Step 5: Outcome scenarios
- If the temperature lands in the 65°F to 69°F range: $10 at $0.20 per share = 50 shares → $50 return → $40 profit
- If the temperature lands in the 60°F to 64°F range: Position pays $0 → $10 loss
- If you sell when price moves to $0.35 before resolution: 50 shares × $0.35 = $17.50 return → $7.50 profit
Common Mistakes in Weather Market Trading
Mistake 1: Not checking the resolution source
Trading a weather market without knowing the exact station and data source used for resolution is the most common and most costly mistake. Two weather stations in the same city can show temperature readings several degrees apart. If you research the wrong station, your analysis is worthless regardless of how accurate it is.
Always read the Rules section before placing any weather trade.
Mistake 2: Using outdated forecast data
Weather forecasts change as model runs update. A forecast you checked at 6 AM may be meaningfully different by noon. For same-day temperature markets, the most recent model run is the most relevant. Always check the timestamp of the forecast you are using.
Mistake 3: Ignoring forecast uncertainty
A point forecast of 62°F does not mean the temperature will be exactly 62°F. Professional forecasters communicate uncertainty through the Forecast Discussion and through ensemble model outputs. Markets that are well-priced for the point forecast may still offer edge in adjacent ranges if the uncertainty is larger than the market prices imply.
Mistake 4: Over-concentrating on single markets
Weather outcomes have inherent uncertainty regardless of how good the forecast is. A single large position in one temperature market can be wiped out by an unexpected weather event that no model predicted. Distributing positions across many markets at small sizes manages this risk more effectively.
Mistake 5: Ignoring fees on certain market types
Weather temperature markets on Polymarket generally do not carry the special taker fees that apply to 15-minute crypto markets. However, some market types do carry fees. Always verify the fee structure for any market before trading, as fees reduce your effective return on all positions.
Mistake 6: Trading high-uncertainty distant forecasts
A seven-day temperature forecast has significantly more uncertainty than a one-day forecast. Markets for temperature events more than three to four days out should be approached with much more caution because professional forecasts become substantially less reliable beyond 72 hours. The edge from model accuracy shrinks considerably as the forecast horizon extends.
The Role of Liquidity in Weather Markets
Not all weather markets have the same liquidity. Liquidity affects your ability to enter and exit positions at fair prices.
High-liquidity markets
Daily temperature markets for major cities like NYC, LA, London, and Shanghai tend to have the deepest liquidity, with multiple active buyers and sellers and tight bid-ask spreads. These markets are easiest to trade at fair prices.
Low-liquidity markets
Niche markets for smaller cities or unusual weather events may have very little trading activity. In these markets, the bid-ask spread can be wide, meaning you pay significantly more than fair value when buying and receive less than fair value when selling. Low-liquidity markets are harder to exit before resolution and carry higher transaction costs.
Practical guidance: For new traders, focus on the highest-volume temperature markets for major cities. As you build experience, you can explore less liquid markets where larger edges sometimes exist, but accept that exiting before resolution may be difficult.
Managing Risk Across Your Weather Trading Portfolio
Position sizing
As a general starting framework
- Limit any single weather market position to 2% to 5% of your total trading capital
- This ensures that even a full loss on a single trade does not materially harm your overall portfolio
- With a $100 trading budget, maximum position size per market should be $2 to $5
Diversification across markets and time horizons
Spread positions across multiple cities and multiple resolution dates. Weather events in New York and Los Angeles are largely independent. A surprise cold snap in New York that costs you a position there does not affect your London or Seoul markets.
Keeping records
Track every trade including the market, the outcome you bought, the price paid, the NOAA forecast at the time, and the resolution result. This record allows you to measure the accuracy of your analysis over time and identify which market types produce the best results for your approach.
Conclusion: Weather Markets Are One of Polymarket's Most Systematic Opportunities
Weather markets stand out on Polymarket because they resolve against objective, publicly verifiable data and the professional forecasting tools needed to analyze them are entirely free.
The opportunity exists because market prices are set by a mix of sophisticated and casual traders, and the casual majority does not systematically cross-reference NOAA or ECMWF data before placing trades. That information gap between what professional meteorological science says and what the market prices reflect is where consistent edges emerge.
The key principles that experienced weather market traders follow are:
- Always read the resolution source in the Rules section before analyzing any market
- Use the same data source for your analysis as the market uses for resolution
- Focus on temperature range markets with clear professional forecast signals
- Trade with small individual position sizes spread across many markets
- Monitor model updates (GFS updates four times per day, ECMWF updates twice) and check for markets that have not repriced to the latest forecast run
- Start with 24-hour and 48-hour markets where forecasts are most reliable before extending to longer time horizons
Weather markets are not a guaranteed profit source. Unexpected weather events, forecast errors, and fast-moving market repricing all create real risk. But they are among the most transparent and data-driven markets on the platform, making them accessible to any trader willing to spend time learning the correct sources and developing a systematic approach.
Frequently Asked Questions
What are Polymarket weather markets?
Polymarket weather markets are prediction market contracts where you buy shares in specific weather outcome buckets priced between $0.01 and $0.99. If your outcome is correct when the market resolves, each share pays $1.00. The price of each outcome reflects the collective probability that all traders assign to it. Weather markets cover daily temperature ranges for global cities, hurricane formations, precipitation events, tornado activity, and long-range climate outcomes.
How much money do I need to start trading weather markets on Polymarket?
You can start with as little as $10 to $20 in USDC. Weather market positions can be as small as $0.01 per share or $1 to $5 total per market. Starting small allows you to learn the mechanics and test your analysis approach without significant financial exposure. Only increase position sizes after you have built confidence in reading forecasts and comparing them to market prices correctly.
Are there fees on Polymarket weather markets?
Polymarket weather temperature markets generally do not carry the special taker fees that apply to some other market categories such as 15-minute crypto direction markets. However, fee structures can change and may vary by specific market type. Always check the fee information for any specific market before trading. Standard Polygon network transaction costs also apply to all on-chain settlements.
Can I exit a weather market position before it resolves?
Yes. You can sell your position at any time before resolution if there is liquidity in the market. If you bought a temperature range at $0.20 and the price has risen to $0.45 based on updated forecasts, you can sell and lock in a $0.25 per share profit without waiting for resolution. This is particularly useful in high-liquidity markets where the bid-ask spread is tight. In low-liquidity markets, exiting before resolution may be difficult or may require accepting a significant discount on the current price.
What is the biggest risk in weather market trading?
The biggest risk is placing trades in markets where the actual resolution source differs from what you analyzed. Checking the wrong weather station can lead to a confident trade based on completely irrelevant data. The second biggest risk is forecast error: even professional models are wrong, and temperature forecasts can miss by several degrees, particularly for days more than 72 hours ahead. Position sizing controls this risk more effectively than trying to improve forecast accuracy.
Disclaimer: This article is for educational purposes only. All prediction market trading involves real financial risk. Always conduct your own research before placing any trade.
