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Weather contracts are the best-kept secret on Kalshi. While sports markets get all the attention (and 90% of the volume), temperature and weather event contracts are quietly the most systematically exploitable markets on the platform.

Why? Because the data that determines the outcome — weather model output — is public, free, and underutilized by the average trader.

Here is everything you need to know to start trading Kalshi weather contracts with a real data edge.

How Kalshi Weather Contracts Work

Kalshi temperature contracts ask a simple yes/no question, like: “Will the high temperature in Chicago on July 22 exceed 85°F?”

You buy YES or NO shares. Each share costs between 1¢ and 99¢ and pays $1 if you are right. The market price reflects the crowd’s implied probability.

Example: If YES is trading at 40¢, the market thinks there is a 40% chance Chicago hits 86°F+ on that date. If you think the real probability is 60%, you have edge.

The Data Stack (All Free)

Before you place a single trade, you need these data sources. All of them are free:

1. National Weather Service (NWS) Point Forecasts Go to weather.gov, enter the city and state, and get the official NWS forecast for the exact resolution station. Kalshi contracts resolve against specific weather stations — always check which one.

2. NOAA Climate Data Online Historical temperature data going back decades. For any date and station, you can calculate the base rate: how often has July 22 in Chicago exceeded 85°F in the last 30 years? If the answer is 25% and Kalshi is pricing at 40%, the market is too bullish.

3. Tropical Tidbits — GEFS Ensemble Plumes This is where the real edge lives. The GEFS (Global Ensemble Forecast System) runs 31 variations of the same weather model with slightly different initial conditions. The ensemble spread tells you the range of possible outcomes. If 25 of 31 members predict below 85°F and Kalshi is pricing YES at 40¢, the market is mispriced.

4. METAR Observations For same-day contracts, live airport weather observations (METAR) are gold. Updated hourly, they tell you the current temperature and trend. A temperature plateau at 2 PM with cloud cover means peak heating is done — the high for the day is likely already set.

Step-by-Step: Analyzing a Temperature Contract

Let us walk through a real example. Say Kalshi has a contract: “NYC Central Park high temperature on July 24: 88°F or above.”

Step 1: Check the base rate. NOAA data shows that July 24 has exceeded 88°F at Central Park in 7 of the last 30 years. Base rate: 23%.

Step 2: Check the NWS forecast. The NWS point forecast for Central Park predicts a high of 86°F. That is below the contract threshold.

Step 3: Check the GEFS ensemble. Tropical Tidbits shows 22 of 31 ensemble members predicting below 88°F. Model-implied probability: ~29% chance of exceeding 88°F.

Step 4: Compare to the Kalshi price. Kalshi YES is trading at 52¢ — implying 52% probability. Your model says 23-29%.

Verdict: Sell YES (buy NO). The market is overpricing this outcome by roughly 25 percentage points.

Common Mistakes Beginners Make

Trusting weather apps. Weather.com and your iPhone widget show a single forecast number with no probability distribution. They are useless for probability estimation.

Ignoring the resolution station. Kalshi contracts resolve against specific weather stations (usually airport stations with METAR reporting). The weather at your house is not the same as the weather at JFK Airport. Always use the correct station data.

Trading too far out. Contracts more than 5–7 days from resolution are dominated by noise. Weather models lose skill rapidly beyond 5 days. The sweet spot for edge is 24–72 hours before resolution, when short-range models (HRRR, NAM) have high accuracy.

Overbetting on single contracts. Most temperature contracts have $5K–$35K in total volume. You cannot scale a $100K position into a $10K market. Size your bets to the available liquidity.

Building a Weather Trading Bot

Once you understand the manual process, you can automate it:

1. Data ingestion: Python script pulls NWS, NOAA, and GEFS data daily
2. Signal generation: Compare model-implied probability to Kalshi market price
3. Threshold check: Only trade when the gap exceeds 10 percentage points
4. Order placement: Kalshi API places limit orders (Maker = lower fees)
5. Exit: Sell at 70% of max gain or hold to settlement

The full code for this bot is available in our tools section. But honestly? Start manually. The bot only works if your model works — and you will not know if your model works until you trade 50+ contracts manually and track your edge.

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The Bottom Line

Kalshi weather markets are the closest thing to a “data edge” a retail trader can access in 2026. The information is public, the models are free, and the competition is mostly retail traders who check the weather app once and buy whatever “feels right.”

That is your edge. Use it.

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