Historical Weather Scraper

hipersoft/historical-weather-scraper

Download historical weather for any place or coordinates: daily or hourly temperature, precipitation, wind and more, back to 1940. One tidy row per day or hour, exported as JSON, CSV or Excel for research, energy, agriculture and analytics.

Run this scraper →See the API call

Total users

Monthly users

Total runs

Per row scraped

$0.0002

Last rebuilt

Oct 26, 2026

Overview

Download historical weather data for anywhere on Earth with the Historical Weather Scraper. Enter place names or coordinates and a date range, and export daily or hourly temperature, precipitation, wind and more — back to 1940 — as one tidy row per day or hour in JSON, CSV or Excel.

Built for climate and academic research, energy and demand forecasting, agriculture and insurance, and data analytics, it turns decades of reanalysis weather into a clean, tabular dataset you can drop straight into a spreadsheet, notebook or model.

What it does

📅 Deep history — daily or hourly weather back to 1940 for any location.
🌡️ Rich variables — temperature (max/min/mean), precipitation, rain, snowfall, wind, gusts, radiation and evapotranspiration.
🧱 Tidy rows — one row per day (or per hour), with each variable as its own column.
📍 Name or coordinates — type "London" or pass exact coordinates; batch many at once.
🕒 Local or UTC — timestamps in each location's local time or UTC.
📤 Export anywhere — JSON, CSV or Excel, or straight into a notebook, sheet or model.

Example output

{
  "name": "London",
  "date": "2024-01-01",
  "temperature_2m_max": 11.4,
  "temperature_2m_min": 6.2,
  "temperature_2m_mean": 8.7,
  "precipitation_sum": 4.9,
  "windspeed_10m_max": 28.1,
  "timezone": "Europe/London"
}

How to use it

Add place names and/or exact coordinates.
Set a start date and end date (YYYY-MM-DD) and choose daily or hourly.
Run, and download the results as JSON, CSV or Excel.

Input fields

Field	Description
Places (by name)	Place names to resolve and look up (e.g. Singapore, London).
Locations (by coordinates)	Exact `{name, latitude, longitude}` objects.
Start / End date	Date range as YYYY-MM-DD (data from 1940 onward).
Granularity	Daily (one row per day) or Hourly (one row per hour).
Daily variables	Which daily metrics to include (temp, precipitation, wind…).
Hourly variables	Which hourly metrics to include when using hourly granularity.
Timezone	`auto` for local time, or an IANA name like `UTC`.
Max rows	Cap on total rows across all locations (0 = no limit).

Output fields

Location columns — name, query, latitude, longitude, country, admin1, elevation, timezone, granularity — plus date (daily) or datetime (hourly) and one column per requested variable (e.g. temperature_2m_max, precipitation_sum, windspeed_10m_max).

Popular use cases

Climate & academic research — build long time series for any site.
Energy & demand forecasting — correlate load with historical temperature and wind.
Agriculture & insurance — analyze rainfall, frost and heat over past seasons.
Analytics & modeling — feed clean historical weather into notebooks and models.
Reporting — compare this year to prior years for any location.

FAQ

Do I need an account or key? No. Enter your locations and dates and run.

How far back does the data go? From 1940 up to a few days before today.

Daily or hourly — which should I pick? Daily gives one compact row per day; hourly gives 24 rows per day with finer detail (larger output).

Can I pull many locations at once? Yes — add multiple place names or coordinate objects; each returns its own rows.

Can I export to Excel or Google Sheets? Yes — results download as JSON, CSV or Excel and drop straight into spreadsheets and notebooks.

Structured historical weather data — daily and hourly temperature, precipitation and wind since 1940 — clean and export-ready for research, energy, agriculture and analytics.

Input

Field	Type	Description
`places` Places (by name)	array	Place names to look up, one per line (e.g. "Singapore", "London", "New York"). Each name is resolved to coordinates automatically. Use this OR the Locations field below (you can combine both).
`locations` Locations (by coordinates)	array	Exact coordinates as a list of objects: {"name": "Farm A", "latitude": 1.29, "longitude": 103.85}. The optional name is used as a label; latitude and longitude are required.
`startDate` Start date	string	First day of history to fetch, as YYYY-MM-DD (e.g. 2024-01-01). Data is available from 1940 onward. Default: `"2024-01-01"`
`endDate` End date	string	Last day of history to fetch, as YYYY-MM-DD. History runs up to a few days before today. Default: `"2024-12-31"`
`granularity` Granularity	string · daily, hourly	Daily gives one row per day (with your daily variables). Hourly gives one row per hour (with your hourly variables) — larger output. Default: `"daily"`
`daily` Daily variables	array	Daily variables (used when Granularity is Daily), one per line. Options include temperature_2m_max, temperature_2m_min, temperature_2m_mean, precipitation_sum, rain_sum, snowfall_sum, windspeed_10m_max, windgusts_10m_max, shortwave_radiation_sum, et0_fao_evapo Default: `["temperature_2m_max", "temperature_2m_min", "temperature_2m`
`hourly` Hourly variables	array	Hourly variables (used when Granularity is Hourly), one per line (e.g. temperature_2m, relativehumidity_2m, precipitation, windspeed_10m, cloudcover, surface_pressure). Default: `["temperature_2m", "precipitation", "windspeed_10m"]`
`timezone` Timezone	string	Timezone for the returned timestamps. Use "auto" to match each location's local time, or an IANA name like "Asia/Singapore" or "UTC". Default: `"auto"`
`maxItems` Max rows	integer	Maximum rows to collect across all locations (0 = no limit). Default: `0`
`proxyConfiguration` Proxy (optional)	object	Optional proxy settings for the run. Not required — the data is fetched over plain HTTP. Default: `{"useApifyProxy": false}`

API

curl -X POST "https://api.apify.com/v2/acts/hipersoft~historical-weather-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "places": [
    "Singapore",
    "London"
  ],
  "locations": []
}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("hipersoft/historical-weather-scraper").call(run_input={
    "places": [
        "Singapore",
        "London"
    ],
    "locations": []
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('hipersoft/historical-weather-scraper').call({
  "places": [
    "Singapore",
    "London"
  ],
  "locations": []
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Pricing

Event	Price	Paid plans from
Row scraped One day or hour of historical weather	$0.0002	$0.0001

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.

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