Climate Data Explorers
Compare eight interactive weather and climate data explorers by geography, provider, variables, observation frequency, strengths and limitations.
Climate Explorer brings together official observations and climatological summaries from national and international providers. Use this page to select the dataset that matches your location, timescale and research question. Each tool is interactive and allows you to inspect stations before downloading or interpreting the data.
Start with your question
- For near-real-time observations across Europe, begin with EuroMeteo.
- For high-frequency national observations, use DWD for Germany, Météo-France for France, JMA for Japan or IMGW for Poland.
- For global station histories, use GHCNh for hourly data or GHCNm for monthly climate series.
- For a 1991–2020 climate baseline, use WMO Normals.
Compare the explorers
EuroMeteo
- Geographic coverage
- Europe and nearby reporting stations.
- Data provider
- MeteoGate and E-SOH participating networks.
- Available variables
- Air temperature, precipitation, sea-level pressure and wind speed.
- Observation frequency
- Near-real-time hourly observations, with availability varying by station and parameter.
- Best use cases
- Comparing recent conditions across European countries and monitoring the latest station reports.
- Important limitations
- Coverage and reporting continuity vary because the explorer combines participating station networks; it is not a homogeneous long-term climate archive.
DWD
- Geographic coverage
- Germany, organized by federal state and weather station.
- Data provider
- Deutscher Wetterdienst Climate Data Center.
- Available variables
- Temperature, precipitation, wind, solar radiation and other station-dependent measurements.
- Observation frequency
- 10-minute, hourly, daily, monthly, annual and multi-annual summaries.
- Best use cases
- German extreme-weather analysis, local station histories and comparisons across timescales.
- Important limitations
- Periods, parameters and resolutions differ by station; recent records may still undergo quality control.
Météo-France
- Geographic coverage
- Metropolitan France and supported overseas departments, organized by department and station.
- Data provider
- Météo-France Open Data.
- Available variables
- Temperature, precipitation, wind, humidity, pressure and station-dependent measurements.
- Observation frequency
- 6-minute, hourly, daily and monthly datasets.
- Best use cases
- Studying French weather events, precipitation intensity and local climate records.
- Important limitations
- Variables and historical coverage vary by dataset and station; the finest-resolution feed is not available everywhere.
JMA
- Geographic coverage
- Japan, organized by prefecture and observation station.
- Data provider
- Japan Meteorological Agency.
- Available variables
- Temperature, precipitation, wind, sunshine and other station-dependent observations.
- Observation frequency
- 10-minute, hourly, daily and monthly datasets.
- Best use cases
- Examining short-lived events and longer station records across Japan.
- Important limitations
- Available elements and record lengths differ by station and temporal resolution; source labels may require local geographic context.
IMGW
- Geographic coverage
- Poland and its national meteorological station network.
- Data provider
- Institute of Meteorology and Water Management – National Research Institute (IMGW-PIB).
- Available variables
- Temperature, precipitation, wind, pressure and other station-dependent measurements.
- Observation frequency
- Hourly, daily and monthly datasets.
- Best use cases
- Polish station comparisons, event analysis and historical weather investigation.
- Important limitations
- Station availability and variables change by resolution, and recent observations may be revised by the provider.
WMO Normals
- Geographic coverage
- Global stations submitted through the World Meteorological Organization normals collection.
- Data provider
- World Meteorological Organization member submissions.
- Available variables
- Monthly climatological averages and totals, including temperature and precipitation where reported.
- Observation frequency
- Monthly statistics for the 1991–2020 standard normal period.
- Best use cases
- Establishing a station-level seasonal baseline and comparing typical monthly conditions.
- Important limitations
- Normals describe a historical reference period, not current conditions; missing months and variables vary by station.
GHCNh
- Geographic coverage
- Global land stations represented in NOAA’s hourly archive.
- Data provider
- NOAA National Centers for Environmental Information.
- Available variables
- Hourly temperature, precipitation, wind, pressure and other station-dependent observations.
- Observation frequency
- Hourly records, with reporting intervals and historical depth varying by station.
- Best use cases
- Global hourly event analysis and inspection of detailed station observations.
- Important limitations
- The archive is heterogeneous: station moves, gaps, reporting changes and varying quality flags must be considered.
GHCNm
- Geographic coverage
- Global monthly land-station records.
- Data provider
- NOAA National Centers for Environmental Information.
- Available variables
- Monthly mean, maximum and minimum temperature plus precipitation where available.
- Observation frequency
- Monthly climate series extending over station-dependent historical periods.
- Best use cases
- Long-term climate trends, monthly anomalies and comparisons between station histories.
- Important limitations
- Coverage is uneven through time and space; users must distinguish raw and adjusted series and avoid treating one station as representative of a broad region.
Before interpreting station data
Station observations describe conditions at a specific instrument and site. Elevation, exposure, urban development, station moves, missing observations and provider quality-control practices can all affect comparisons. Consult the related guide and provider documentation before using a series as evidence for a wider region or long-term trend.