Weather Forecast Scraper — Global Forecasts & History by Place or Coordinates

Get global weather forecasts and recent history for any location as clean, structured JSON, CSV, Excel or XML. Look up locations by place name (Singapore, London, New York) or by exact latitude/longitude, and pull hourly and daily series — temperature, precipitation, wind, humidity, cloud cover and more. One run, one tidy row per location, ready for apps, dashboards, research and automation.

What does the Weather Forecast Scraper do?

This actor turns weather data into a structured dataset. Give it a list of place names or coordinates, choose the hourly and daily variables you care about, and it returns a flat record per location: resolved coordinates, timezone, elevation and the full forecast series for the days you requested. Add past days to include recent history alongside the forecast. Export the results in any format the Apify platform supports, or fetch them straight through the API.

What data can you get?

Data point	Description
Location name & query	The place label and the original search term.
Resolved coordinates	Latitude and longitude used for the forecast.
Country & region	Country and first-level region for geocoded places.
Elevation	Ground elevation in metres.
Timezone	IANA timezone, abbreviation and UTC offset.
Hourly series	Requested hourly variables (e.g. temperature, precipitation, wind) with timestamps.
Daily series	Optional daily aggregates (e.g. max/min temperature, precipitation sum).
Units	Unit for every returned variable.
Coverage window	Number of hours/days plus first and last timestamp.

Use cases

• Live dashboards — power a weather widget or operations dashboard for many cities at once.
• Forecast pipelines — feed structured temperature, rain and wind series into BI tools and models.
• Location monitoring — track conditions across stores, sites, farms or delivery zones.
• Trip & event planning — pull multi-day forecasts for a batch of destinations.
• Research datasets — combine forecast and recent-history data for analysis.

How to use the Weather Forecast Scraper

1. Add the actor to your Apify account and open its input.
2. Enter places (one name per line) and/or exact locations as { "latitude": ..., "longitude": ... } objects.
3. Choose the hourly variables (and optional daily ones), set forecastDays, a timezone, and optional pastDays.
4. Click Run.
5. Export the results as JSON, CSV, Excel or XML, or fetch them through the Apify API.

Input

{
  "places": ["Singapore", "London"],
  "locations": [{ "name": "HQ", "latitude": 1.29, "longitude": 103.85 }],
  "hourly": ["temperature_2m", "precipitation", "windspeed_10m"],
  "daily": ["temperature_2m_max", "temperature_2m_min", "precipitation_sum"],
  "forecastDays": 7,
  "timezone": "auto",
  "pastDays": 0
}

Field	Description
places	Place names to resolve to coordinates, one per line.
locations	Exact coordinates as objects with latitude, longitude and an optional name.
hourly	Hourly variables to include (defaults to temperature, precipitation, wind speed).
daily	Optional daily aggregate variables.
forecastDays	Days of forecast from today forward (1–16).
timezone	auto for each location's local time, or an IANA name like Asia/Singapore.
pastDays	Days of recent past weather to include before today (0–92).
proxyConfiguration	Standard Apify proxy settings for the run (optional).

What you get

One record per location, ready to export to JSON, CSV, Excel or XML.

{
  "name": "Singapore",
  "query": "Singapore",
  "latitude": 1.3,
  "longitude": 103.86,
  "country": "Singapore",
  "admin1": null,
  "elevation": 27,
  "timezone": "Asia/Singapore",
  "timezoneAbbreviation": "GMT+8",
  "utcOffsetSeconds": 28800,
  "forecastDays": 3,
  "pastDays": 0,
  "hourlyVariables": "temperature_2m,precipitation,windspeed_10m",
  "dailyVariables": null,
  "hours": 72,
  "days": 0,
  "startTime": "2026-08-31T00:00",
  "endTime": "2026-09-02T23:00",
  "hourlyUnits": { "temperature_2m": "°C", "precipitation": "mm", "windspeed_10m": "km/h" },
  "hourly": {
    "time": ["2026-08-31T00:00", "2026-08-31T01:00"],
    "temperature_2m": [27.4, 27.1],
    "precipitation": [0.0, 0.2],
    "windspeed_10m": [6.1, 5.4]
  },
  "dailyUnits": null,
  "daily": null,
  "scrapedAt": "2026-08-31T09:15:04.123Z"
}

Output schema

Field	Type	Description
name	string	Location label (geocoded name or the coordinate's given name).
query	string	Original place search term, when applicable.
latitude	number	Latitude used for the forecast.
longitude	number	Longitude used for the forecast.
country	string	Country of a geocoded place.
admin1	string	First-level region (state/province) of a geocoded place.
elevation	number	Ground elevation in metres.
timezone	string	IANA timezone of the returned timestamps.
timezoneAbbreviation	string	Short timezone label (e.g. GMT+8).
utcOffsetSeconds	number	Offset from UTC in seconds.
forecastDays	number	Number of forecast days requested.
pastDays	number	Number of past days included.
hourlyVariables	string	Comma-separated hourly variables returned.
dailyVariables	string	Comma-separated daily variables returned.
hours	number	Count of hourly timesteps.
days	number	Count of daily timesteps.
startTime	string	First timestamp in the series.
endTime	string	Last timestamp in the series.
hourlyUnits	object	Unit for each hourly variable.
hourly	object	Hourly series: parallel arrays keyed by variable.
dailyUnits	object	Unit for each daily variable.
daily	object	Daily series: parallel arrays keyed by variable.
scrapedAt	string	ISO timestamp when the row was produced.

Related Actors

• CoinGecko Scraper — live cryptocurrency prices, market cap and volume.
• Yahoo Finance Scraper — quotes and OHLCV history for stocks, ETFs and forex.
• OpenStreetMap POI Scraper — points of interest by area and category.

FAQ

Can I look up many locations in one run? Yes. Add as many place names and coordinate objects as you like; each one becomes its own row in the dataset.

Which weather variables can I request? Common hourly variables such as temperature_2m, precipitation, windspeed_10m, relativehumidity_2m, cloudcover and weathercode, plus daily aggregates like temperature_2m_max, temperature_2m_min and precipitation_sum.

Can I get past weather too? Yes — set pastDays to include recent history before today alongside the forecast.

Does it use local time? Set timezone to auto to return each location's local time, or pass an IANA name like Asia/Singapore or UTC.

Can I connect this to n8n? Yes. Run the actor from n8n with the Apify node (or an HTTP Request node against the Apify API), trigger it on a schedule, and pipe the structured weather rows straight into your workflow — Slack alerts, spreadsheets, databases and more.

Can I connect this to my other tools? Yes. It works with almost any cloud service or web app through integrations on the Apify platform — Make, Zapier, Slack, Google Drive and many more, plus the Apify API, JavaScript/Python clients and MCP. Or use webhooks to trigger an action whenever a run finishes.

What export formats are available? JSON, CSV, Excel and XML, plus programmatic access through the Apify API.

Notes

Original clean-room implementation. Returns public weather data; you are responsible for compliant use. All trademarks belong to their respective owners.