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Global Historical Climatology Network Monthly - Version 2

GHCN-Monthly Version 2 Ascii Text Files

The Global Historical Climatology Network (GHCN-Monthly) database contains historical temperature, precipitation, and pressure data for thousands of land stations worldwide. The period of record varies from station to station, with several thousand extending back to 1950 and monthly updates for several hundred stations via CLIMAT reports. The data are available without charge through NCEI's anonymous FTP service. Effective May 2, 2011, the Global Historical Climatology Network-Monthly (GHCN-M) version 3 dataset of monthly mean temperature has replaced GHCN-M version 2 as the dataset for operational climate monitoring activities for temperature. The formal designation is ghcnm.x.y.z[optionally -betan].yyyymmdd.

Both historical and near-real-time GHCN data undergo rigorous quality assurance reviews. These reviews include preprocessing checks on source data, time series checks that identify spurious changes in the mean and variance, spatial comparisons that verify the accuracy of the climatological mean and the seasonal cycle, and neighbor checks that identify outliers from both a serial and a spatial perspective.

GHCN-Monthly is used operationally by NCEI to monitor long-term trends in temperature and precipitation. It has also been employed in several international climate assessments, including the Intergovernmental Panel on Climate Change 4th Assessment Report, the Arctic Climate Impact Assessment, and the "State of the Climate" report published annually by the Bulletin of the American Meteorological Society.

Data Description

One of the primary goals of GHCN-Monthly was to acquire additional data in order to enhance spatial and temporal coverage. There were three reasons for this goal: data for recent months allows one to assess current climatic conditions and place them in historical perspective, denser coverage facilitates the analysis of regional climate change, and certain areas (or certain times in certain areas) are under-sampled even from the perspective of a global analysis. Because numerous institutions operate weather stations and because no single repository archives all of the data for all stations, five acquisition strategies were employed to maximize the available pool of data: contacting data centers, exploiting personal contacts, tapping related projects, conducting literature searches, and distributing miscellaneous requests. As a result, GHCN-Monthly contains data from dozens of diverse sources.

Temperature Data Sources & Precipitation Data Sources Tables

Table 1 - Temperature Data Sources
Data Source Number of Mean Temperature Stations Number of Max/Min Temperature Stations
NCAR's World Monthly Surface Station Climatolgoy 3,563 0
NCEI's Maximum/Minimum Temperature Dataset 3,179 3,179
Deutscher Wetterdienst's Global Monthly Surface Summaries Dataset 2,559 0
Monthly Climatic Data for the World 2,176 0
World Weather Records (1971-80) 1,912 0
World Weather Records (1961-70) 1,858 0
U.S. Summary of the Day dataset 1,463 1,463
U.S. Historical Climatology Network 1,221 1,221
A Climatological Database for Northern Hemisphere Land Areas 920 0
Australian National Climate Center's Dataset for Australia 785 785
North American Climate Data, NCEI 764 764
Bo-Min's Dataset for the People's Republic of China 378 0
USSR Network of CLIMAT stations 243 0
Daily Temperature and Precipitation Data for 223 USSR Stations (NDP-040) 223 223
Two Long-Term Databases for the People's Republic of China (NDP-039) 205 60
ASEAN Climatic Atlas 162 162
Pakistan's Meteorological and Climatological Dataset 132 132
Diaz's Dataset for High-Elevation Areas 100 0
Douglas' Dataset for Mexico 92 0
Ku-nil's dDataset for Korea 71 71
Jacka's Dataset for Antarctic Locales 70 0
Monthly Data for the Pacific Ocean / Western Americas 60 0
U.S. Historical Climatology Network (Alaska) 47 47
Muthurajah's Dataset for Malaysia 18 18
Hardjawinata's Dataset for Indonesia 13 13
Fitzgerald's Dataset for Ireland 11 11
Sala's Dataset for Spain 3 0
Al-kubaisi's Dataset for Qatar 1 1
Al-sane's Dataset for Kuwait 1 1
Stekl's dDataset for Ireland 1 1
Table 2 - Precipitation Data Sources
Data Source Number of
African Historical Precipitation Data 1,239
ASEAN Climatic Atlas 868
Bo-min's Dataset for the People's Republic of China 378
Brewster's Dataset for Australia/Oceana 2,369
Canadian Climatological Data 848
Comprehensive Pacific Rainfall Dataset 303
Davidson's Dataset for Mexico 30
Earnest's Dataset for the Amazon Basin 925
Garcia's Dataset for Argentina 157
Griffith's Colonial Archives Dataset 251
Groisman's Dataset of the former USSR 610
Hulme's Dataset of the world 11,785
Ku-nil's Dataset for Korea 71
Monthly Climate Data for the World 2,000
NCAR's Dataset for India 4,602
NCAR's Dataset for South America 678
Nichol's Dataset for Australia 191
Non-African Historical Precipitation Data 1,164
Oladipo's Dataset for Nigeria 13
Roucou's Dataset for Africa 69
Sinica's Dataset for China 336
Two Long-Term Databases for the People's Republic of China 265
Waylen's Dataset for Panama 61
Waylen's Dataset for Costa Rica 329
Wernstedt's Dataset for the world 4,659
U.S. Historical Climatology Network 1,221
























































GHCN-Monthly contains mean temperature data for 7,280 stations and maximum/minimum temperature data for 4,966 stations. All have at least 10 years of data. The archive also contains homogeneity-adjusted data for a subset of this network (5,206 mean temperature stations and 3,647 maximum/minimum temperature stations). The homogeneity-adjusted network is somewhat smaller because at least 20 years of data were required to compute reliable discontinuity adjustments. Adequate assessment of the homogeneity of some isolated stations was not possible. Precipitation data are available for 20,590 stations and sea level pressure data for 2,668 stations. In general, the best spatial coverage is evident in North America, Europe, Australia, and parts of Asia. Likewise, coverage in the Northern Hemisphere is better than the Southern Hemisphere.

Temperature Methods

The following journal articles describe the methods used in developing the GHCN-Monthly Temperature dataset:

Precipitation Methods

  • Duplicate Elimination
    Scientists can frequently GHCN obtain a precipitation time series for a given station more than one source. For example, rainfall data for Beijing were available in three different source datasets. In brief, comparing each station with all the other stations in all source datasets identified duplicate stations. The description of similarity between stations uses several statistics, including the number of identical months of data, the length of the longest run of identical months, and the number of identical values that were zero. Use of these diagnostic statistics, in conjunction with station metadata subjectively determine whether station were duplicates. In most cases, the decision was relatively straightforward, although a few degenerate time series posed proved more challenging.
  • Quality Control
    Staff employed a variety of tests to assess data quality. The first step involved comparing stations with a gridded climatology and plotting the stations for visual inspection. Both of these processes uncovered mislocated stations and the digitized formerly uncovered stations 6 months out of phase. Additionally, each time series was tested for significant discontinuities using the Cumulative Sum test (which looks for changes in the mean) and an analogous test that looks for changes in the variance or scale. Evaluation of each time series for runs of three or more months of the same nonzero value. Finally, scientists evaluated each individual precipitation total to determine if it was an outlier in space and/or time using a variety of nonparametric statistics.

Version 2 Bias Correction Software

The automated bias correction software (Peterson and Easterling, 1994; Easterling and Peterson, 1995) used to detect and adjust for documented and undocumented inhomogeneities in the GHCN-Monthly version 2 monthly temperature dataset.

Please refer to the README file in this directory for information on this software.

  • Easterling, D. R., and T. C. Peterson, 1995: A new method for detecting undocumented discontinuities in climatological time series. International Journal of Climatology, 15, 369-377, doi:10.1002/joc.3370150403.
  • Peterson, T. C., and D. R. Easterling, 1994: Creation of homogeneous composite climatological reference series. International Journal of Climatology, 14, 671-679, doi:10.1002/joc.3370140606.


For questions specific to GHCNM, please email

Citing and Metadata

Information is available on the how users can cite the dataset and view the Metadata.