How Accurate Are NWS Daily High Temperature Forecasts? A 5-Year Analysis

If you've ever planned a beach day around tomorrow's forecast and ended up sunburned (or shivering), you've experienced a real and quantifiable gap: weather forecasts have measurable error.

But how much? Most weather coverage talks about accuracy in vague terms — "generally reliable" or "improving year over year." Almost no one publishes the actual numbers.

We analyzed 5 years (2021-2025) of National Weather Service forecast vs. actual daily high temperature data for four major U.S. cities. Here's what we found.

The summary numbers

Across all 4 stations and ~7,300 days, NWS daily high forecasts at the 24-hour horizon have:

• RMSE: 3.32°F (root mean square error — average miss with bigger errors penalized more)
• MAE: 2.45°F (mean absolute error — typical day's miss)
• Bias: +0.05°F (essentially zero — forecasts neither systematically over- nor under-predict)

That's better than most people guess. A typical forecast misses by about 2-3°F. Big misses do happen, but they're rare.

Per-city accuracy

Different cities have wildly different forecast difficulty.

| City | RMSE | MAE | Bias | Hardest months | |---|---|---|---|---| | Miami (KMIA) | 2.18°F | 1.62°F | -0.08 | December, January | | Los Angeles (KLAX) | 3.21°F | 2.45°F | +0.12 | September (Santa Ana season) | | New York (KNYC) | 3.68°F | 2.78°F | +0.21 | March (spring transition) | | Chicago (KMDW) | 3.85°F | 2.91°F | -0.05 | March-April (frontal season) |

Why Miami is so accurate

Miami has the most stable climate of the four. Daily highs cluster tightly around climatology — in July, Miami's typical high is 90.5°F ± 2.0°F. Even a mediocre forecast does well because the underlying signal is consistent.

When Miami forecasts do miss, it's usually around frontal passages in winter (when N/NW winds push down from the continent) or hurricane-influenced periods. Outside those windows, prediction is straightforward.

Why Chicago is hardest

Chicago sits in the path of major frontal systems and Great Lakes effects. A cold front passing at noon vs. 4 PM gives wildly different highs for the same day. Lake breezes can suppress afternoon highs by 5-15°F on otherwise warm days.

These dynamics are inherently chaotic. Even sophisticated models struggle. 8°F RMSE for Chicago in winter isn't poor forecasting — it's the irreducible uncertainty of the underlying weather.

When forecasts are most wrong

Looking at the worst 5% of forecast misses (>8°F error) across all cities, three patterns dominate:

1. Frontal timing errors

When a cold front arrives 4-6 hours earlier or later than expected, the daily high shifts dramatically. This is the single biggest source of large errors at NYC and Chicago.

2. Convective activity

Thunderstorms cool the boundary layer rapidly. A surprise afternoon storm can knock 5-8°F off the predicted high. Most common in May-August across all four cities.

3. Regime transitions

Early spring and late fall see frequent regime shifts (winter pattern → spring pattern). The model's recent-bias correction takes 5+ days to recalibrate. The worst errors cluster in March-April and October-November.

What this means in practice

A few practical implications:

For trip planning: Trust forecasts within ±3°F most days. Don't over-plan around exact numbers — "high near 75" really means "probably 72-78."

For energy planning: Heating/cooling demand estimates based on forecasts should assume ±3°F uncertainty. That's a meaningful capacity buffer.

For prediction markets: Kalshi temperature markets price 1°F-wide brackets (e.g., "high between 88-89°F"). With NWS forecasts at 3°F RMSE, a single forecast number doesn't pinpoint one bracket — it spreads probability across 3-5 brackets. Markets that price one bracket near 100% confidence are usually overpriced.

For climate research: The systematic bias is essentially zero (+0.05°F across 7,300 days). This is good news — NWS forecasters aren't shifted hot or cold on average.

A surprising finding from the data

We expected forecast accuracy to be roughly similar across cities, with maybe small variations. The actual difference is substantial — Miami forecasts are 75% more accurate than Chicago forecasts by RMSE.

This matters because most weather services and apps quote one accuracy number ("3°F average error") without distinguishing by location. That number is wildly misleading for both Miami (where 2°F is realistic) and Chicago (where 4°F is realistic).

If you live somewhere with stable weather (coastal California, Florida, much of the Southwest), trust forecasts more. If you live somewhere with active frontal weather (Midwest, Great Plains, Northeast), build in more uncertainty.

Methodology

For replication: we used NOAA GHCN-Daily for actual daily TMAX values and the Iowa Environmental Mesonet's archive of NWS forecast products for historical forecast data. The "24-hour forecast" refers to the high temperature for tomorrow as predicted by NWS at the previous day's evening package. Stations: USW00012839 (Miami), USW00023174 (Los Angeles), USW00094728 (New York Central Park), and KMDW (Chicago Midway).

The analysis script and full results are available on request — happy to share with anyone interested in replicating or extending.

Want this analysis on live markets?

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