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Downloads climate/weather data from Copernicus Climate Data Store (CDS) using Python cdsapi. Covers ERA5, ERA5-Land, seasonal forecasts, API setup, and optimized download scripts.
npx claudepluginhub jinwx/weather-data-skills --plugin weather-data-skillsHow this skill is triggered — by the user, by Claude, or both
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/weather-data-skills:cds-downloadThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill helps users download data from the Copernicus Climate Data Store (CDS) efficiently. It covers setup, dataset selection, and writing optimized Python download scripts.
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This skill helps users download data from the Copernicus Climate Data Store (CDS) efficiently. It covers setup, dataset selection, and writing optimized Python download scripts.
The CDS (https://cds.climate.copernicus.eu) is the main portal for Copernicus climate data.
Reference files — read the relevant one when you need dataset-specific details:
references/api-setup.md — Account setup, .cdsapirc configuration, cdsapi installation. Read this when the user needs help getting started or has authentication issues.references/era5.md — ERA5 and ERA5-Land datasets: identifiers, variables, storage types, efficiency strategies, and download script usage. Read this for any ERA5/ERA5-Land download task.references/seasonal.md — Seasonal forecast datasets: identifiers, parameters, system versioning, efficiency strategies, and download script usage. Read this for seasonal prediction downloads.A scripts/ directory contains reusable download scripts for ERA5 and seasonal forecasts — see the reference files for usage. These scripts cover the common ERA5/ERA5-Land and C3S seasonal forecast workflows. For other CDS datasets, unsupported parameter combinations, or custom post-processing, read the relevant script source as a reference and write a tailored script.
When a user asks to download CDS data, follow this sequence:
Before writing any download script, confirm the user has CDS access configured:
.cdsapirc file configured? If unsure, read references/api-setup.md for the current format and walk them through it.cdsapi installed (pip install "cdsapi>=0.7.7")? The version matters — older versions use incompatible syntax.If the user seems experienced (they mention specific dataset names, already have scripts), skip the hand-holding and go straight to their request.
If the user isn't sure which dataset they need, help them narrow it down. The CDS catalog (https://cds.climate.copernicus.eu/datasets) has hundreds of datasets organized by these dimensions:
Product type — What kind of data?
Variable domain — What part of the Earth system?
Spatial coverage — Global or regional (Europe, Arctic, Antarctic)?
Temporal coverage — Past, present, or future?
Walk the user through these questions to narrow down the right dataset. If they already know the general category (e.g., "I need reanalysis surface temperature"), you can go straight to the reference file. If the search is open-ended, browse the CDS catalog together or fetch the catalog page to find matching datasets.
For datasets not covered by the reference files, go to https://cds.climate.copernicus.eu/datasets/<dataset-identifier> to check the dataset description and download form. The "Show API request code" button on the download form generates correct API syntax — use this as the authoritative source for variable names and parameter formats.
This is where most of the value lies. Read the relevant dataset reference file first — each one contains dataset-specific efficiency strategies. The optimal request structure varies significantly between datasets because they use different storage backends and archival layouts.
General principles that apply to all CDS downloads:
area to subset geographically when the user doesn't need global datagrid to request coarser resolution if full resolution isn't neededFor dataset-specific efficiency (request splitting, parallelization, grouping strategies), always consult the reference file. What works for ERA5 (split by month) can be counterproductive for seasonal forecasts (combine into fewer, larger requests).
Help the user choose if they're unsure:
| NetCDF | GRIB | |
|---|---|---|
| Best for | Analysis in Python (xarray), sharing | Operational meteorology, WRF input |
| Ecosystem | xarray, netCDF4, pandas | cfgrib, eccodes, wgrib2 |
| File size | Larger | Smaller (packed) |
| Readability | Self-describing, easy to inspect | Requires specialized tools |
| CDS default | No (must specify) | Yes |
Default recommendation: NetCDF for most research users, GRIB for operational/NWP workflows. Note that some datasets (e.g., seasonal forecasts) work better in their native GRIB format — NetCDF conversion can be lossy or slow.
Before finalizing any script, verify key details. The CDS API will reject requests with invalid parameter names, so accuracy matters:
references/seasonal.md for details and code examples. This is far more reliable than hardcoding system versions.https://cds.climate.copernicus.eu/datasets/<dataset-identifier>) and use the download form. The "Show API request code" button at the bottom generates correct, verified API syntax..cdsapirc or old cdsapi version. See references/api-setup.md.