Best Months by Climate Normals

Typical weather per month from 1991–2020 normals, and the best months for an outdoor job.

Try an example: Berlin: best months for drying laundry outside · Munich: best months to paint the house · Hamburg: barbecue months

More about this tool

How it works

Finds the nearest weather station with official 1991–2020 climate normals — Deutscher Wetterdienst for Germany, NOAA for the United States — and rates every month for drying laundry, painting, mowing, barbecues or walks from its typical temperature and how often it rains.

Germany: the nearest of 1,090 Deutscher Wetterdienst climate stations (within 40 km) from the bundled 1991–2020 multi-annual means — monthly mean temperature, precipitation and sunshine; precipitation and sunshine come from the same station or the nearest one within 15 km.
United States: the nearest NOAA NCEI station with 1991–2020 monthly normals, found with the NCEI search service in a box of ±0.4° (then ±1.2°) around the place; daily maximum and minimum temperature, precipitation and days with at least 0.10 inch of precipitation are converted to °C and mm. NCEI answers are cached for 30 days.
Each month gets a temperature fit from the mean temperature (0 below the activity’s lower limit, 1 in its ideal band, falling to 0 six degrees above the upper limit) and a dryness: 1 − wet days / days in month (NOAA), or for DWD stations (no wet-day count) the relative sunshine duration n/N — the sunshine normal per day divided by the maximum possible sunshine at mid-month (FAO-56 eq. 34) — or, without a sunshine normal, 1 − precipitation / 150 mm. Score = 100 × fit × dryness.
Bands (mean temperature, °C): drying 4–14 ideal to 30; painting 8–15 to 24; mowing 6–12 to 26; barbecue 10–18 to 27; walk −2–10 to 22. The best months are those within 10 points of the top score.

Normals describe the average of 30 years, not next year’s weather. Ratings are a planning heuristic; for the actual day use the forecast tools.

Sources

DWD Climate Data Center: multi-annual means 1991–2020 (Deutscher Wetterdienst)
NOAA NCEI U.S. Climate Normals 1991–2020 (monthly) (NOAA National Centers for Environmental Information)

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How to use Best Months by Climate Normals

Use official 1991–2020 climate normals to pick the months that typically suit painting, drying laundry outside, mowing, barbecues or walks.

What it is for

A forecast only reaches about nine days. When you plan further ahead — "when should we repaint the house next year?" — the best evidence is what the weather is usually like. This tool finds the nearest station with official 1991–2020 climate normals and rates every month for an outdoor job from its typical temperature and how often it rains.

Two openly licensed sources are used: Deutscher Wetterdienst (1,090 climate stations in Germany, CC BY 4.0) and NOAA's U.S. Climate Normals (public domain). For places elsewhere it says so plainly instead of guessing. Normals describe a 30-year average; any single year can be warmer, colder or wetter.

Worked example

Painting a house in New York. Inputs: place 40.78, -73.97, activity Exterior painting or staining. The tool picked "NEW YORK CNTRL PK TWR, NY US", 0.1 km away, and answered "Best months for exterior painting or staining near NEW YORK CNTRL PK TWR, NY US: May, June, August, September, October."

Worked example
Month	Mean (°C)	Max / min (°C)	Wet days	Score
April	12	16.6 / 7.5	6.5	45
May	17.3	21.9 / 12.8	7.3	76
July	25.3	29.4 / 21.2	6.9	61
September	20.7	24.6 / 16.8	6.2	79
October	14.4	18.1 / 10.8	5.6	75

September scores best: warm enough, and with about six wet days the driest of the warm months. July loses points because its mean of 25.3 °C is above the painting band, where heat makes paint dry too fast. April is still too cool on average. December to March score zero.

In Germany the same job for Munich used the DWD station München-Bogenhausen (1.6 km) and rated June, July and August best — there the dryness comes from the sunshine normals, because DWD normals have no wet-day count.

How to read the result

Station and distance tell you whose climate you are looking at. A station far away or at a different height can differ noticeably.
Score (0–100) is the temperature fit times the dryness. Compare months at one place; scores from DWD and NOAA stations are not directly comparable, because their dryness is measured differently.
Best months are all months within 10 points of the top score.
The table shows the raw normals: mean temperature, maximum and minimum (NOAA), precipitation, wet days (NOAA) and sunshine hours (DWD).

Tips and common mistakes

Choose the activity before you compare: drying laundry tolerates cooler months than painting.
Coastal and mountain places differ from a station 30 km away; read the warning when the station is more than 25 km off.
Use this tool to pick the month, then the forecast tools to pick the day.
Precipitation amount is not the same as rainy days: a summer thunderstorm month can have a lot of rain on few days.

FAQ

Why only Germany and the United States?

Those normals have clear open licences. Other national normals often come with non-commercial or unclear terms, so they are not used.

What does dryness mean for German stations?

The share of the possible sunshine that a month typically gets (relative sunshine duration), calculated with the FAO-56 day-length formula for the station's latitude.

Are the DWD numbers the official ones?

Yes, they are DWD's multi-annual means 1991–2020, merged per station (precipitation and sunshine from the same or a station within 15 km).

Does the tool use my exact address?

The place is rounded to about one kilometre; only the nearest station's data is shown.

Guide last updated: November 7, 2026

For scripts and AI agents

JSON API: POST https://hearthclimatemonths.usefulweb.app/api/v1/calculate (same calculation as this page)
Interface description: OpenAPI 3.1 · AI guide: llms.txt
MCP server (Streamable HTTP): https://hearthclimatemonths.usefulweb.app/mcp · tool name hearth_climate_months

Example request

curl -s -X POST https://hearthclimatemonths.usefulweb.app/api/v1/calculate \
  -H 'content-type: application/json' \
  -d '{"place":"52.47, 13.40","activity":"drying"}'

Feedback · ideas & bug reports welcome