Access Climate Hazards Center Datasets • chirps

Overview

chirps provides access to Climate Hazards Center (CHC) datasets, CHIRPS v3 and v2 for rainfall and CHIRTS-daily and CHIRTS-ERA5 for temperature. Data are read directly from the CHC server, either as native rasters for custom spatial workflows or as time series for points and polygons.

For climate indices computed from these data (dry spells, heavy rain days, growing degree days, crop sensitive indices), use the climatrends package, which takes the output of chirps directly.

Available Datasets

Dataset	Variable	Resolution	Coverage	Period
CHIRPS v3 (default)	Precipitation	0.05°	60°S to 60°N	1981 to near present
CHIRPS v2	Precipitation	0.05° / 0.25°	50°S to 50°N	1981 to December 2026 (retired)
CHIRTS-daily	Tmax, Tmin, RHum, HeatIndex	0.05°	60°S to 70°N	1983 to 2016
CHIRTS-ERA5	Tmax, Tmin	0.05°	60°S to 70°N	1980 to near present

ESI and IMERG are also available through the ClimateSERV API with get_esi() and get_imerg(). Both will be deprecated in a future release.

Applications

These datasets are commonly used for:

Crop modeling
Climate risk assessment
Agricultural monitoring
Seasonal analysis
Environmental characterization
Ecological and biodiversity studies

Quick start

From CRAN

The stable version is available through CRAN.

install.packages("chirps")

From GitHub

A development version that may have new features or bug fixes is available through GitHub.

library("remotes")

install_github("ropensci/chirps", build_vignettes = TRUE)

Time series for points and polygons

get_chirps() reads the CHIRPS v3 files from the CHC server and returns one row per location and day. Fetch rainfall for three points across the Tapajós National Forest (Brazil) in January 2017.

library("chirps")

lonlat = data.frame(lon = c(-55.0281, -54.9857, -55.0714),
                    lat = c(-2.8094, -2.8756, -3.5279))

dates = c("2017-01-01", "2017-01-31")

rain = get_chirps(lonlat, dates)

Each call states what it downloads, for example daily rainfall from CHIRPS v3.0 or daily maximum temperature from CHIRTS, so a mix-up between get_chirps() and get_chirts() is easy to spot. Set options(chirps.quiet = TRUE) to hide these messages.

A single date returns one day. With sf polygons, each day is averaged over the cells of each polygon, and operation sets another summary (4 for the sum).

data("tapajos", package = "chirps")

rain_area = get_chirps(tapajos, dates, operation = 4)

Temperature works the same way with get_chirts(). CHIRTS-daily ends in 2016, so use product = "CHIRTS-ERA5" for recent years.

tmax = get_chirts(lonlat, c("2024-01-01", "2024-01-31"), var = "Tmax",
                  product = "CHIRTS-ERA5")

Climate indices with climatrends

With as.matrix = TRUE, chirps returns one row per location and one column per day, named by date. This is the input of climatrends::rainfall() and climatrends::temperature().

library("climatrends")

rain = get_chirps(lonlat, dates, as.matrix = TRUE)

rainfall(rain, day.one = dates[1], span = ncol(rain))

precip_indices() remains in chirps as a simple example. Use climatrends for analysis.

Raw raster products

The *_raw() functions return the CHC files as terra::SpatRaster objects, with one layer per day. This suits large areas and custom extraction workflows.

library("terra")

dates = c("2017-11-15", "2017-11-20")

# CHIRPS v3 precipitation, "rnl" (default) or "sat"
chirps_v3 = get_chirps_raw(dates = dates, type = "sat")

# CHIRTS-ERA5 minimum and maximum temperature
tmin = get_chirts_raw(dates = dates, var = "Tmin", product = "CHIRTS-ERA5")
tmax = get_chirts_raw(dates = dates, var = "Tmax", product = "CHIRTS-ERA5")

pts = vect(lonlat, geom = c("lon", "lat"), crs = "EPSG:4326")

rain = extract(chirps_v3, pts)

Servers

The CHC server is the default and the recommended source. server = "ClimateSERV" is kept for backward compatibility. It serves CHIRPS v2 only, which CHC retires after the December 2026 release, and it is slow for more than a few points. ClimateSERV support for CHIRPS may be removed in a future release.

CHC blocks addresses that request many files that do not exist. chirps checks the first and last file of each request before reading the rest and identifies itself with a user agent. Keep requests within the period each product covers.

Going further

The full functionality of chirps is illustrated in the package vignette. The vignette can be found on the package website or from within R once the package has been installed, e.g. via:

vignette("Overview", package = "chirps")

Citing Data

While chirps does not redistribute data, users should cite the original data providers when using CHIRPS, CHIRTS, CHIRTS-ERA5, or CHIRPS v3 products.

When using datasets obtained through this package, please cite the original data providers.

CHIRPS v3

Funk, C., Peterson, P., Harrison, L. et al. (2026). The Climate Hazards Center Infrared Precipitation with Stations, Version 3. Scientific Data, 13, 718. https://doi.org/10.1038/s41597-026-07096-4

CHIRTS-ERA5

CHIRTS-ERA5 Data Repository https://doi.org/10.15780/G2F08J (2025). Data was accessed on [DATE].

CHIRPS v2

Funk C., Peterson P., Landsfeld M., … Michaelsen J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66

CHIRTS

Verdin, A., Funk, C., Peterson, P., Landsfeld, M., Tuholske, C., and Grace, K. (2020). Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set. Scientific Data, 7, 303. https://doi.org/10.1038/s41597-020-00643-7

chirps: Access Climate Hazards Center Datasets