chirps: Access Climate Hazards Center Datasets

Overview

chirps provides access to Climate Hazards Center (CHC) datasets including CHIRPS, CHIRPS v3, CHIRTS-daily, and CHIRTS-ERA5. Data can be retrieved either as extracted values for points and polygons or as native raster products for custom spatial workflows.

Available Datasets

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)

Example

Accessing raw raster products

The package can provide direct access to CHC climate datasets as terra::SpatRaster objects. This is useful when working with large spatial datasets or custom extraction workflows.

library("chirps")
library("terra")

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

# CHIRPS v2 precipitation
chirps_v2 = get_chirps_raw(dates = dates, version = "2.0")

# CHIRPS v3 precipitation
chirps_v3 = get_chirps_raw(dates = dates, version = "3.0", type = "sat")

# CHIRTS-ERA5 minimum temperature
tmin = get_chirts_era5_raw(dates = dates, var = "Tmin")

# CHIRTS-ERA5 maximum temperature
tmax = get_chirts_era5_raw(dates = dates, var = "Tmax")

Raster values can be extracted using functions from the terra package. Fetch CHIRPS data from three points across the Tapajós National Forest (Brazil) in January 2017.

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

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

rain = extract(chirps_v2, pts)

The resulting object contains daily values for each location and can be readily converted to a matrix or data frame for further analysis.

The recommended workflow is to retrieve native raster products using get_chirps_raw(), get_chirts_raw(), and get_chirts_era5_raw(), and then use the terra package for extraction and spatial analysis.

Accessing high-level extraction

get_chirps() default procedure will download the COG files from the CHIRPS server and handle it internally using the package terra. This is more interesting when dealing with hundreds of points and days. Data can be returned as a matrix using the argument as.matrix = TRUE.

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")

dat = get_chirps(lonlat, dates, server = "CHC", as.matrix = FALSE)

For a faster download of few datapoints (~ 10 datapoints), the argument server = "ClimateSERV" can be used

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")

dat = get_chirps(lonlat, dates, server = "ClimateSERV", as.matrix = FALSE)

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

Meta

• Please report any issues or bugs.

• License: MIT.

• Get citation information for chirps in R by typing citation(package = "chirps").

• You are welcome to contribute to the chirps project. Please read our contribution guidelines.

• Please note that the chirps project is released with a a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.