U.S. and Global Precipitation
Identification
1. Indicator Description
This indicator describes changes in total precipitation over land for the United States from 1901 to 2020
and the world from 1901 to 2019. In this indicator, precipitation data are presented as trends in
anomalies. Precipitation is an important component of climate, and changes in precipitation can have
wide-ranging direct and indirect effects on the environment and society. As average temperatures at the
Earth's surface rise, more evaporation occurs, which, in turn, increases overall precipitation. Therefore,
a warming climate is expected to increase precipitation in many areas. However, factors such as shifting
wind patterns and changes in the ocean currents that drive the world's climate system will also cause
some areas to experience decreased precipitation.
Components of this indicator include:
• Changes in precipitation in the contiguous 48 states over time (Figure 1).
• Changes in worldwide precipitation over land through time (Figure 2).
• A map showing rates of precipitation change across the contiguous 48 states and Alaska (Figure
3).
2. Revision History
April 2010: Indicator published.
December 2012: Updated indicator with data through 2011.
August 2013: Updated indicator with data through 2012.
June 2015: Updated Figures 1 and 3 with data through 2014; updated Figure 2 with data
through 2013.
August 2016: Updated indicator with data through 2015.
April 2021: Updated indicator with data through 2020 for Figures 1 and 3 and through 2019 for
Figure 2.
Data Sources
3. Data Sources
This indicator is based on precipitation anomaly data provided by the National Oceanic and Atmospheric
Administration's (NOAA's) National Centers for Environmental Information (NCEI), formerly the National
Climatic Data Center (NCDC). Specifically, this indicator uses the following NCEI data sets:
• Figure 1, contiguous 48 states precipitation; Figure 3, precipitation map: nClimDiv.
• Figure 2, global precipitation: Global Historical Climatology Network-Monthly (GHCN-M) Version
4.
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nClimDiv is itself based on data from the daily version of GHCN (GHCN-Daily). These data undergo more
extensive processing by NCEI on a monthly basis for inclusion in nClimDiv.
4. Data Availability
All of the underlying data sets can be accessed online, along with descriptions and metadata. Specific
data sets were obtained as follows.
Contiguous 48 States Time Series
Precipitation time series data for the contiguous 48 states (Figure 1) are based on nClimDiv data that
were obtained from NCEI's "Climate at a Glance" web interface (www.ncdc.noaa.gov/cag). For access to
underlying nClimDiv data and documentation, see: www.ncdc.noaa.gov/monitoring-
references/maps/us-climate-divisions.php.
Global Time Series
GHCN global precipitation data (Figure 2) are not available from NCEI's web interface, but the time
series shown in this indicator is also presented in NCEI's annual "State of the Climate" analysis, which is
published in a special edition of the Bulletin of the American Meteorological Society every summer. EPA
obtained the most recent data from NCEI staff, reflecting GHCN global precipitation anomalies through
2019. A version of this analysis appears in "State of the Climate in 2019" (Blunden and Arndt, 2020).
However, the analysis shown in EPA's indicator differs because it covers a longer period ("State of the
Climate" only displays this particular data set from 1979 forward) and because it reflects a slightly
different set of data availability criteria in order to allow a suitable long-term time series to be
generated. For access to underlying GHCN-M Version 4 data and documentation, see:
www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-
climatology-network-monthlv-version-4. Note that as of July 2020, NCEI was still in the process of
making the new GHCN v4 processed precipitation data set fully available through the public web
interface.
Contiguous 48 States and Alaska Map
The map in this indicator (Figure 3) is based on nClimDiv monthly data by climate division, which are
publicly available from NCEI at: www.ncdc.noaa.gov/cag.
Methodology
5. Data Collection
This indicator is based on precipitation measurements collected from thousands of land-based weather
stations throughout the United States and worldwide, using standard meteorological instruments. Data
for the contiguous 48 states and Alaska were compiled in the nClimDiv data set. Data for the rest of the
world were taken from GHCN data sets. All of the networks cited here are overseen by NOAA, and their
methods of site selection and quality control have been extensively peer reviewed. As such, they
represent the most complete long-term instrumental data sets for analyzing recent climate trends. More
information on these networks can be found below.
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Contiguous 48 States Time Series; Contiguous 48 States and Alaska Map
The nClimDiv divisional data set incorporates precipitation data from GHCN-Daily stations in the
contiguous 48 states and Alaska. This data set includes stations that were previously part of the U.S.
Historical Climatology Network (USHCN), as well as additional stations that were able to be added to
nClimDiv as a result of quality-control adjustments and digitization of paper records. Altogether,
nClimDiv incorporates data from more than 10,000 stations. These stations are spread among 357
climate divisions in the contiguous 48 states and Alaska.
In addition to incorporating more stations, the nClimDiv data set differs from the USHCN because it
incorporates a grid-based computational approach known as climatologically-aided interpolation
(Willmott and Robeson, 1995), which helps to address topographic variability. Data from individual
stations are combined in a grid that covers the entire contiguous 48 states and Alaska with 5-kilometer
resolution. These improvements have led to a new data set that maintains the strengths of its
predecessor data sets while providing more robust estimates of area averages and long-term trends.
To learn more about nClimDiv, see: www.ncdc.noaa.gov/news/ncdc-introduces-national-temperature-
index-page. www.ncdc.noaa.gov/monitoring-references/maps/us-climate-divisions.php. and Vose et al.
(2014). Also see Vose et al. (2017) for details of the more recent effort to apply nClimDiv methods to
Alaska.
Global Time Series
GHCN Version 4 contains monthly climate data from 26,000 weather stations worldwide. Data were
obtained from many types of stations. The shorter version of this time series published in Blunden and
Arndt (2020) is based on a filtered subset of stations that are missing no more than 30 percent of data
points during the base period and the periods before and after. However, to enable a longer time series
to be generated for this indicator, NCEI recommended and applied a 50 percent filter instead of 30
percent.
NCEI has published documentation for the GHCN. For more information, including data sources,
methods, and recent improvements, see: www.ncdc.noaa.gov/data-access/land-based-station-
data/land-based-datasets/global-historical-climatology-network-monthlv-version-4 and the sources
listed therein.
6. Indicator Derivation
Contiguous 48 States and Global Time Series
NOAA calculated monthly precipitation totals for each site. In populating the GHCN and nClimDiv, NOAA
employed a homogenization algorithm to identify and correct for substantial shifts in local-scale data
that might reflect changes in instrumentation, station moves, or urbanization effects. These adjustments
were performed according to published, peer-reviewed methods. For more information on these quality
assurance and error correction procedures, see Section 7.
In this indicator, precipitation data are presented as trends in anomalies. An anomaly represents the
difference between an observed value and the corresponding value from a baseline period. This
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indicator uses a baseline period of 1901 to 2000 for the contiguous 48 states and global data, and a
baseline period of 1925 to 2000 for Alaska data due to sparse data prior to 1925. The choice of baseline
period will not affect the shape or the statistical significance of the overall trend in anomalies. For
absolute anomalies in inches, it only moves the trend up or down on the graph in relation to the point
defined as "zero."
To generate the precipitation time series, NOAA converted total annual precipitation measurements,
measured in millimeters, into anomalies. EPA converted NOAA's final results from millimeters to inches.
To achieve uniform spatial coverage (i.e., not biased toward areas with a higher concentration of
measuring stations), NOAA calculated area-weighted averages of grid-point estimates interpolated from
station data. The precipitation time series for the contiguous 48 states (Figure 1) is based on the
nClimDiv gridded data set, which reflects a high-resolution (5-kilometer) interpolated grid that accounts
for station density and topography. See: wwwl.ncdc.noaa.gov/pub/data/cirs/climdiv/divisional-
readme.txt for more information. The global graph (Figure 2) comes from an analysis of grid cells
measuring 5 degrees by 5 degrees. See: www.ncdc.noaa.gov/temp-and-precip/ghcn-gridded-products
for more information.
Figures 1 and 2 show trends from 1901 to 2020 and 1901 to 2019, respectively, based on NOAA's
gridded data sets. Although earlier data are available for some stations, 1901 was selected as a
consistent starting point.
Contiguous 48 States and Alaska Map
The map in Figure 3 shows the overall change in precipitation over the United States for the period from
1901 to 2020, except for Alaska, for which widespread and reliable data collection did not begin until
1925 (therefore the map shows 1925-2020 for Alaska). Hawaii and U.S. territories are not included in
this figure, due to insufficient data completeness or length of the measurement record. This map is
based on NOAA's nClimDiv gridded analysis, with results averaged within each climate division. The
slope of each precipitation trend was calculated from annual climate division anomalies (in inches) by
ordinary least-squares regression, then multiplied by the length of the entire period of record to get
total change in inches. The total change was then converted to percent change, using average
precipitation during the standard baseline period (1901-2000 for the contiguous 48 states; 1925-2000
for Alaska) as the denominator.
Indicator Development
NOAA initially released the nClimDiv data set in 2014, which allowed this indicator to use climate
divisions in Figure 3 and a high-resolution climate division-based gridded analysis for Figure 1. Previous
versions of EPA's indicator presented a contiguous 48 states time series and a United States map based
on a coarse grid analysis, which was the best analysis available from NOAA at the time.
NOAA is continually refining historical data points in the GHCN and nClimDiv, often as a result of
improved methods to reduce bias and exclude erroneous measurements. As EPA updates this indicator
to reflect these upgrades, slight changes to some historical data points may become apparent. No
attempt has been made to portray data beyond the time and space in which measurements were made.
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7. Quality Assurance and Quality Control
NCEI's databases have undergone extensive quality assurance procedures to identify errors and biases in
the data and to either remove these stations from the time series or apply correction factors.
Contiguous 48 States Time Series; Contiguous 48 States and Alaska Map
The nClimDiv data set follows the USHCN's methods to detect and correct station biases brought on by
changes to the station network over time. The transition to a grid-based calculation did not significantly
change national averages and totals, but it has led to improved historical temperature values in certain
regions, particularly regions with extensive topography above the average station elevation-
topography that is now being more thoroughly accounted for. An assessment of the major impacts of
the transition to nClimDiv can be found at: www.ncdc.noaa.gov/monitoring-references/docs/GrDD-
Transition.pdf.
Global Time Series
QA/QC procedures for GHCN precipitation data are described at: www.ncdc.noaa.gov/data-access/land-
based-station-data/land-based-datasets/global-historical-climatology-network-monthlv-version-4.
GHCN data undergo rigorous quality assurance reviews, which include pre-processing checks on source
data; removal of duplicates, isolated values, and suspicious streaks; time series checks that identify
spurious changes in the mean and variance; spatial comparisons to verify the accuracy of the
climatological mean and the seasonal cycle; and neighbor checks to identify outliers from both a serial
and a spatial perspective.
Analysis
8. Comparability Over Time and Space
Both nClimDiv and the GHCN have undergone extensive testing to identify errors and biases in the data
and either remove these stations from the time series or apply scientifically appropriate correction
factors to improve the utility of the data. In particular, these corrections address advances in
instrumentation and station location changes. See Section 7 for documentation.
Contiguous 48 States Time Series; Contiguous 48 States and Alaska Map
All GHCN-Daily stations are routinely processed through a suite of logical, serial, and spatial quality
assurance reviews to identify erroneous observations. For nClimDiv, all such observations were set to
"missing" before computing monthly values, which in turn were subjected to additional serial and
spatial checks to eliminate residual outliers. Stations having at least 10 years of valid monthly data since
1950 were used in nClimDiv.
For more documentation of nClimDiv methods, see:
wwwl.ncdc.noaa.gov/pub/data/cirs/climdiv/divisional-readme.txt.
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Global Time Series
The GHCN applied stringent criteria for data homogeneity in order to reduce bias. In acquiring data sets,
the original observations were sought. See "Quality Assurance and Quality Control" for documentation.
9. Data Limitations
Factors that may impact the confidence, application, or conclusions drawn from this indicator are as
follows:
1. Biases in measurements may have occurred as a result of changes over time in instrumentation,
measuring procedures, and the exposure and location of the instruments. Where possible, data
have been adjusted to account for changes in these variables. For more information on these
corrections, see Section 7.
2. As noted in Section 10, uncertainties in precipitation data increase as one goes back in time, as
there are fewer stations early in the record. However, these uncertainties are not sufficient to
undermine the fundamental trends in the data.
10. Sources of Uncertainty
Uncertainties in precipitation data increase as one goes back in time, as there are fewer stations early in
the record. However, these uncertainties are not sufficient to undermine the fundamental trends in the
data.
Error estimates are not readily available for U.S. or global precipitation. Vose and Menne (2004) suggest
that the station density in the U.S. climate network is sufficient to produce a robust spatial average.
11. Sources of Variability
Annual precipitation anomalies naturally vary from location to location and from year to year as a result
of normal variation in weather patterns, multi-year climate cycles such as the El Nino-Southern
Oscillation and Pacific Decadal Oscillation, and other factors. This indicator accounts for these factors by
presenting a long-term record (more than a century of data) and averaging consistently over time and
space.
12. Statistical/Trend Analysis
This indicator uses ordinary least-squares regression to calculate the slope of the observed trends in
precipitation. A simple t-test indicates that the following observed trends are significant at the 95-
percent confidence level:
• Contiguous 48 states precipitation, 1901-2020: +0.020 inches/year (p < 0.001).
• Global precipitation, 1901-2019: +0.010 inches/year (p < 0.001).
Among the individual climate divisions shown in Figure 3, 43 percent of divisions have statistically
significant precipitation trends, based on ordinary least-squares linear regression and a 95-percent
confidence threshold.
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References
Blunden, J., and D.S. Arndt (eds.). 2020. State of the climate in 2019. B. Am. Meteorol. Soc. 101(8):Si-
S429. https://doi.Org/10.1175/2020BAMSStateoftheClimate.l.
Vose, R.S., and M.J. Menne. 2004. A method to determine station density requirements for climate
observing networks. J. Climate 17(15):2961-2971.
Vose, R.S., S. Applequist, M. Squires, I. Durre, M.J. Menne, C.N. Williams, Jr., C. Fenimore, K. Gleason,
and D. Arndt. 2014. Improved historical temperature and precipitation time series for U.S. climate
divisions. J. Appl. Meteorol. Clim. 53:1232-1251. https://iournals.ametsoc.org/doi/pdf/10.1175/JAMC-
D-13-0248.1.
Vose, R.S., M. Squires, D. Arndt, I. Durre, C. Fenimore, K. Gleason, M.J. Menne, J. Partain, C.N. Williams,
Jr., P.A. Bieniek, and R.L. Thoman. 2017. Deriving historical temperature and precipitation time series for
Alaska climate divisions via climatologically aided interpolation. J. Serv. Climatol. 10(1).
https://stateclimate.org/pdfs/iournal-articles/2017-Ross-etal.pdf.
Willmott, C.J., and S.M. Robeson. 1995. Climatologically aided interpolation (CAI) of terrestrial air
temperature. Int. J. Climatol. 15(2):221-229.
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