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Introduction to chirps

Kauê de Sousa

Department of Agricultural Sciences, University of Inland Norway, Hamar, Norway Digital Inclusion, Bioversity International, Montpellier, France

Adam H. Sparks

Centre for Crop Health, University of Southern Queensland, Toowoomba, Australia

Aniruddha Ghosh

Climate Action, International Center for Tropical Agriculture (CIAT), Nairobi, Kenya
Source: vignettes/Overview.Rmd
Overview.Rmd

Overview

The chirps package provides access to climate datasets produced by the Climate Hazards Center (CHC), University of California Santa Barbara. The package supports both high-level extraction of climate time series for points and polygons, as well as direct access to the original raster products through the terra package.

Supported datasets include:

Dataset Variables Period
CHIRPS v2 Precipitation 1981–present
CHIRPS v3 Precipitation 1981–present
CHIRTS-daily Tmax, Tmin, RHum, HeatIndex 1983–2016
CHIRTS-ERA5 Tmax, Tmin 1980–present

Accessing raw raster products

The recommended workflow is to retrieve climate products as native SpatRaster objects and use functions from terra for subsequent extraction and spatial analysis.

CHIRPS v2 

library(chirps)
library(terra)

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

v2 = get_chirps_raw(dates = dates, version = "2.0")

v2

CHIRPS v3 

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

v3 = get_chirps_raw(dates = dates, version = "3.0", type = "sat")

v3

Available daily CHIRPS v3 products include:

  • "sat": daily totals derived using NASA IMERG
  • "rnl": daily totals derived using ERA5 reanalysis

CHIRTS-ERA5 

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

tmax = get_chirts_era5_raw(dates = dates, var = "Tmax")

tmin = get_chirts_era5_raw(dates = dates, var = "Tmin")

CHIRTS-ERA5 extends the CHIRTS temperature record from 1980 to near-present by combining CHIRTS with ERA5 reanalysis data.

CHIRTS-daily 

dates = c("2010-01-01", "2010-01-10")

tmax = get_chirts_raw(dates = dates, var = "Tmax")

tmin = get_chirts_raw(dates = dates, var = "Tmin")

Available variables include:

  • "Tmax"
  • "Tmin"
  • "RHum"
  • "HeatIndex"

Extracting values using terra

Once data are loaded as rasters, values can be extracted for locations of interest.

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(v3, pts)

head(rain)

The resulting object contains one row per location and one column per raster layer.

Accessing climate time series

For users interested primarily in time series extraction, the package provides high-level functions that handle raster retrieval and extraction internally.

Point locations 

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")

head(dat)

ClimateSERV

For small requests involving a limited number of locations, the ClimateSERV API can be used.

dat = get_chirps(lonlat,
                 dates,
                 server = "ClimateSERV")

Working with terra

Because the package returns native SpatRaster objects, users can directly leverage the terra ecosystem.

# crop to area of interest
r_crop = crop(r, ext(-60, -50, -5, 5))

# calculate mean precipitation
mean_rain = app(r, mean)

# write to disk
writeRaster(r,
            "chirps.tif",
            overwrite = TRUE)

Datasets citation

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

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

Additional resources