ClimData provides a unified interface for extracting climate data from multiple providers (MSWX, CMIP, POWER, DWD, HYRAS), computing extreme indices, and converting results to tabular form. The ClimData (or ClimateExtractor) class is central: it manages configuration, extraction, index computation, and common I/O.

• Provider-agnostic extraction (point / region / shapefile)
• Unit normalization via xclim
• Compute extreme indices using package indices
• Convert xarray Datasets → long-form pandas DataFrames
• Simple workflow runner for chained actions

1. Create and activate a conda environment:

# create
conda create -n climdata python=3.11 -y

# activate
conda activate climdata

2. Install via pip (PyPI, if available) or from source:

# from PyPI
pip install climdata

# or from local source (editable)
git clone <repo-url>
cd climdata
pip install -e .

Install optional extras as needed (e.g., xclim, shapely, hydra, dask):

pip install xarray xclim shapely hydra-core dask "pandas>=1.5"

If you need the imputation functionality (gap filling with ML models), install PyTorch and related packages:

# Install PyTorch CPU version (recommended for most users)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Install additional ML packages for imputation
pip install torch-cluster -f https://data.pyg.org/whl/torch-2.3.1+cpu.html
pip install pytorch-lightning torchmetrics lightning torchcde reformer-pytorch
pip install tensorflow darts sktime prophet tsfel tsfresh transformers timm

For GPU support, see PyTorch installation guide.

from climdata import ClimData  # or from climdata.utils.wrapper_workflow import ClimateExtractor

overrides = [
    "dataset=mswx",
    "lat=52.5",
    "lon=13.4",
    "time_range.start_date=2014-01-01",
    "time_range.end_date=2014-12-31",
    "variables=[tasmin,tasmax,pr]",
    "data_dir=/path/to/data",
    "index=tn10p",
]

# initialize
extractor = ClimData(overrides=overrides)

# extract data (returns xarray.Dataset and updates internal state)
ds = extractor.extract()

# compute index (uses cfg.index)
ds_index = extractor.calc_index(ds)

# convert to long-form dataframe and save
df = extractor.to_dataframe(ds_index)
extractor.to_csv(df, filename="index.csv")

Use run_workflow for multi-step sequences:

result = extractor.run_workflow(actions=["extract", "calc_index", "to_dataframe", "to_csv"])

WorkflowResult contains produced dataset(s), dataframe(s), and filenames.

• See API docs under docs/api/ for detailed descriptions of ClimData/ClimateExtractor methods.
• Examples and notebooks are under examples/.

• Run tests and lint locally.
• Follow project coding and documentation conventions; submit PRs with tests.

If you use climdata in your research or projects, please cite it using the following formats:

@software{muduchuru2024climdata,
  title={climdata: Automated Climate Data Extraction and Processing},
  author={Muduchuru, Kaushik},
  year={2024},
  version={0.5.0},
  url={https://github.com/Kaushikreddym/climdata},
  note={Available at https://Kaushikreddym.github.io/climdata}
}

Muduchuru, K. (2024). climdata: Automated climate data extraction and processing (v0.5.0). Retrieved from https://github.com/Kaushikreddym/climdata

Our repository includes a CITATION.cff file. GitHub will automatically show a "Cite this repository" button with ready-to-use citation formats.

DOI: https://doi.org/10.5281/zenodo.19554926
Zenodo Record: https://zenodo.org/record/19554926

For archival setup details, see Zenodo & DOI Guide.

Refer to the repository LICENSE file for terms.

• Make sure yq is installed:

  brew install yq   # macOS
  # OR
  pip install yq

• To see available variables for a specific dataset (for example mswx), run:

  python download_location.py --cfg job | yq '.mappings.mswx.variables | keys'

• Supports multiple weather data providers
• Uses xarray for robust gridded data extraction
• Handles curvilinear and rectilinear grids
• Uses a Google Drive Service Account for secure downloads
• Easily reproducible runs using Hydra

MSWX (Multi-Source Weather) is released under the CC BY-NC 4.0 license. This means:

✅ Allowed uses:

• Academic research
• Non-profit scientific studies
• Personal projects
• Government or NGO applications (non-commercial)

❌ Not allowed:

• Commercial use or products
• For-profit services

To access MSWX data:

1. Visit https://www.gloh2o.org/mswx/
2. Submit a data request for non-commercial use
3. Once approved, follow the Google Drive API setup below to configure climdata

⚠️ Important: By using MSWX via climdata, you agree to the CC BY-NC 4.0 license terms. Unauthorized commercial use is prohibited.

This project uses the Google Drive API with a Service Account to securely download weather data files from a shared Google Drive folder.

Follow these steps to set it up correctly:

• Go to Google Cloud Console.
• Click “Select Project” → “New Project”.
• Enter a project name (e.g. WeatherDataDownloader).
• Click “Create”.

• In the left sidebar, go to APIs & Services → Library.
• Search for “Google Drive API”.
• Click it, then click “Enable”.

• Go to IAM & Admin → Service Accounts.
• Click “Create Service Account”.
• Enter a name (e.g. weather-downloader-sa).
• Click “Create and Continue”. You can skip assigning roles for read-only Drive access.
• Click “Done” to finish.

• After creating the Service Account, click on its email address to open its details.
• Go to the “Keys” tab.
• Click “Add Key” → “Create new key” → choose JSON → click “Create”.
• A .json key file will download automatically. Store it securely!

• Place the downloaded .json key in the conf folder with the name service.json.

1. Create a free account on the Copernicus Climate Data Store

2. Once logged in, go to your user profile

3. Click on the "Show API key" button

4. Create the file ~/.cdsapirc with the following content:

   url: https://cds.climate.copernicus.eu/api/v2
   key: <your-api-key-here>

5. Make sure the file has the correct permissions: chmod 600 ~/.cdsapirc