Heating degree days and Yearly precipitation
Where Heating degree days is higher, Yearly precipitation tends to be lower. The link is weak: -0.30 across 3,089 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 39 states, the middle value of the link is -0.09.
Holds after adjusting
Every place, plotted
One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.
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What each measure is
Heating degree days
A measure of how cold the year was, as a total over its days. A day whose average temperature is 50°F counts 15 heating degree days, the degrees it fell short of 65°F. A day at 65°F or warmer counts none. Homes often need heat on days below 65°F, so more degree days mean more heating weather. NOAA estimates them from its monthly temperatures for each county. A state's figure is the average of its counties, each weighted by its land area. Alaska and Hawaii are not covered.
NOAA NCEI nClimDiv · NOAA NCEI nClimDiv climdiv-hddccy, sum of the 12 months; states, counties averaged by land area
Yearly precipitation
Rain plus melted snow and ice over the year, in inches, averaged across the whole county or state. It is the total of the twelve months, from NOAA's map of weather station readings.
NOAA NCEI nClimDiv · NOAA NCEI nClimDiv climdiv-pcpncy/pcpnst
Highest and lowest
Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.
Counties
By heating degree days
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Highest | ||
| Lake County, CO | 11,328 | 22.4 |
| San Juan County, CO | 11,210 | 38.9 |
| Summit County, CO | 10,987 | 23.7 |
| Sublette County, WY | 10,945 | 20.3 |
| Hinsdale County, CO | 10,865 | 27.8 |
| Lowest | ||
| Monroe County, FL | 83 | 57.2 |
| Miami-Dade County, FL | 85 | 70 |
| Broward County, FL | 108 | 77 |
| Palm Beach County, FL | 167 | 60.5 |
| Collier County, FL | 167 | 50.8 |
By yearly precipitation
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Highest | ||
| Jefferson County, WA | 6,643 | 95.9 |
| Tillamook County, OR | 5,389 | 93.9 |
| Lincoln County, OR | 4,846 | 82.9 |
| Skamania County, WA | 6,889 | 82.5 |
| Mason County, WA | 5,518 | 82.1 |
| Lowest | ||
| Imperial County, CA | 1,007 | 3.5 |
| Winkler County, TX | 2,078 | 4.32 |
| Ward County, TX | 1,857 | 4.32 |
| El Paso County, TX | 2,272 | 4.66 |
| Yuma County, AZ | 1,207 | 5.08 |
States
By heating degree days
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Highest | ||
| North Dakota | 8,901 | 17.4 |
| Wyoming | 8,670 | 18.9 |
| Minnesota | 8,233 | 24.2 |
| Montana | 8,065 | 18.4 |
| Idaho | 8,000 | 22.1 |
| Lowest | ||
| Florida | 647 | 54.5 |
| Louisiana | 1,414 | 43.7 |
| Georgia | 1,879 | 52 |
| Mississippi | 1,888 | 47.7 |
| Texas | 1,901 | 24 |
By yearly precipitation
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Highest | ||
| Connecticut | 5,223 | 61.5 |
| Massachusetts | 5,501 | 57.7 |
| Rhode Island | 5,071 | 57.1 |
| New Hampshire | 6,926 | 56.3 |
| Florida | 647 | 54.5 |
| Lowest | ||
| New Mexico | 4,654 | 10.9 |
| Arizona | 3,511 | 11 |
| Nevada | 6,196 | 12.9 |
| Utah | 6,668 | 15 |
| North Dakota | 8,901 | 17.4 |
Metro areas
By heating degree days
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Highest | ||
| Breckenridge, CO | 11,117 | 23.2 |
| Jackson, WY-ID | 10,692 | 33.6 |
| Edwards, CO | 9,543 | 21.8 |
| Evanston, WY-UT | 9,512 | 17.3 |
| Cody, WY | 9,355 | 26.1 |
| Lowest | ||
| Key West-Key Largo, FL | 83 | 57.2 |
| Miami-Fort Lauderdale-West Palm Beach, FL | 122 | 68 |
| Naples-Marco Island, FL | 167 | 50.8 |
| Cape Coral-Fort Myers, FL | 218 | 44.9 |
| Port St. Lucie, FL | 219 | 62 |
By yearly precipitation
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Highest | ||
| Port Townsend, WA | 6,643 | 95.9 |
| Newport, OR | 4,846 | 82.9 |
| Shelton, WA | 5,518 | 82.1 |
| Brookings, OR | 4,660 | 81.6 |
| Aberdeen, WA | 5,206 | 78.1 |
| Lowest | ||
| El Centro, CA | 1,007 | 3.5 |
| Yuma, AZ | 1,207 | 5.08 |
| Town of Pecos, TX | 1,948 | 5.62 |
| Las Cruces, NM | 2,766 | 5.79 |
| El Paso, TX | 2,493 | 5.8 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,108 counties: the five with far more yearly precipitation than their heating degree days would suggest, and the five with far less.
| Heating degree days | Yearly precipitation | |
|---|---|---|
| Above the line | ||
| Jefferson County, WA | 6,643 | 95.9 |
| Tillamook County, OR | 5,389 | 93.9 |
| Skamania County, WA | 6,889 | 82.5 |
| Mason County, WA | 5,518 | 82.1 |
| Lincoln County, OR | 4,846 | 82.9 |
| Below the line | ||
| Imperial County, CA | 1,007 | 3.5 |
| Yuma County, AZ | 1,207 | 5.08 |
| La Paz County, AZ | 1,441 | 5.26 |
| Ward County, TX | 1,857 | 4.32 |
| Winkler County, TX | 2,078 | 4.32 |
Two series, one county
Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.
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Every version of the link
Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.
| Before adjusting for anything | -0.487 | 3,089 counties reporting both |
| After adjusting for density, income and education | -0.301 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.297 | size taken out as well as density |
| Also comparing only within each state | -0.033 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Also holding winter sunshine equal | -0.485 | compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more) |
| Giving bigger counties more weight | -0.036 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 284 equally weighted counties |
| Middle value inside single states | -0.088 | the link worked out inside each of 39 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.314 | uses each county’s rank, not its value, so a few extreme counties cannot create the link |
How much to believe it
Could it be chance?
The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 1.8e-65. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.041. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.4e-64.
Do nearby places look alike?
Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.72 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.
Did they change together?
The figures above compare counties at one moment. This asks a different question: where heating degree days rose most, did yearly precipitationmove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 3,108 | -0.17 | -0.07 | 0.637 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,108 | +0.28 | +0.03 | 0.777 |
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Freezing days and Heating degree days — +0.95
- Heating degree days and Winter nightly low — -0.94
- Heating degree days and Summer daily high — -0.85
- Days of 90°F or hotter and Heating degree days — -0.80
- Heating degree days and Winter daily sunlight — -0.74
- Heating degree days and Daily sunlight — -0.70
- Heating degree days and Cloud cover — +0.64
- High cholesterol and Heating degree days — -0.58
Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Heating degree days and Yearly precipitation