GHCNDEX provides gridded, station-based indices of temperature- and precipitation- related climate extremes. It is intended for climate change detection and attribution studies, climate model evaluation, and operational monitoring of extreme climatic events. Twenty-six indices, including daily maximum and minimum temperatures, number of frost days, maximum 1-day precipitation, and growing season length are provided for 1951 to the present at monthly timesteps on a 2.5°x2.5 ° grid. Definitions of these core indices follow recommendations set forth by the CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETTCCDI). Input data are from the Global Historical Climatology Network (GHCN) Daily station data. Updates are made operationally. The 26 indices are computed for each station, and then the indices are gridded using an angular distance weighting scheme. Compared with the precipitation-based indices, the temperature-based indices generally show larger spatial coherence and large-scale averages that are more robust to sampling gaps.
Key Strengths:
- Large number of indices provided calculated with standard, intuitive definitions of extremes
- Operationally updated
Key Limitations:
- Global coverage not as complete as other data sets including more station networks
- No homogenization to account for changes in observing practices, instrumentation, site location or related issues
- Uneven updating; e.g. European and North American observations updated more regularly than African and South American observations
Years of Record
Timestep
Data Time Period Extended?
Domain
Spatial Resolution
Ocean or Land
Missing Data Flag
Vertical Levels
Input Data
Suggested Data Citation
Donat, M.G., L.V. Alexander, H. Yang, I. Durre, R. Vose, J. Caesar, 2013: Global Land-Based Datasets for Monitoring Climatic Extremes. Bull. Amer. Meteor. Soc., 94, 997–1006.
Data Access: Please Cite data sources, following the data providers' instructions.
- Donat, M.G., L.V. Alexander, H. Yang, I. Durre, R. Vose, J. Caesar, 2013: Global Land-Based Datasets for Monitoring Climatic Extremes. Bull. Amer. Meteor. Soc., 94, 997–1006.
- Fischer, E. M., and R. Knutti (2014), Detection of spatially aggregated changes in temperature and precipitation extremes, Geophys. Res. Lett., 41, doi:10.1002/2013GL058499.
