Data HubContribute

Monthly and Seasonal Precipitation from CHIRPS v3 (Africa)

Climate Hazards Center, UC Santa Barbara

Part of: Africa Agriculture Adaptation Atlas

licenseCC-BY-4.0updated2026-09-14

Total precipitation (PTOT) aggregated from CHIRPS v3.0 and delivered as per-pixel monthly Cloud Optimized GeoTIFFs and per-year rolling 3-month (seasonal) sums over Africa, 1981 to present. This is the rainfall backbone of the KE-ENSO Explorer’s seasonal/ENSO analysis: the twelve seasonal windows (JFM, FMA, …, DJF) are the tri-month sums the notebook compares against ENSO phase and against the drought (SPEI), vegetation (NDVI) and flood layers. A derived aggregation product, not a redistribution of the CHIRPS daily archive.

Important note

Source CHIRPS is public domain ("no restrictions on use"); these reprocessed COGs are released CC-BY-4.0 with attribution to the Climate Hazards Center, UC Santa Barbara. Seasonal windows are labelled by the calendar year in which the window ends (DJF_2020 = Dec 2019 to Feb 2020). Edge windows that the record cannot cover in full are absent (NDJ and DJF 1981; windows ending after the latest monthly file), so not every season x year combination of the template exists.

Quick start

Python
import rioxarray

url = "https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=PTOT/PTOT-1981-01.tif"
da = rioxarray.open_rasterio(url, masked=True)

Full access details and endpoints

Intended uses

  • Seasonal and ENSO-phase rainfall analysis and anomaly context in the KE-ENSO Explorer.
  • Rainfall driver for interpreting the drought (SPEI), vegetation (NDVI) and flood layers.

Not recommended for

Daily or sub-monthly rainfall analysis.

Why: Only monthly totals and 3-month sums are published here.

Use instead: The CHIRPS v3 daily archive (catalog record chirps-v3-daily).

Variables

VariableTypeUnitDescription
PTOTfloat32mmTotal precipitation over the period (calendar month, or 3-month seasonal sum).

Dimensions

Axes of the cube — spatial, temporal, and domain axes.

timetemporal

Month of the monthly totals (YYYY-MM); the {time} token of the monthly asset.

1981-011981-021981-031981-041981-051981-061981-071981-081981-091981-101981-111981-121982-011982-021982-031982-041982-051982-061982-071982-081982-091982-101982-111982-121983-011983-021983-031983-041983-051983-061983-071983-081983-091983-101983-111983-121984-011984-021984-031984-041984-051984-061984-071984-081984-091984-101984-111984-121985-011985-021985-031985-041985-051985-061985-071985-081985-091985-101985-111985-121986-011986-021986-031986-041986-051986-061986-071986-081986-091986-101986-111986-121987-011987-021987-031987-041987-051987-061987-071987-081987-091987-101987-111987-121988-011988-021988-031988-041988-051988-061988-071988-081988-091988-101988-111988-121989-011989-021989-031989-041989-051989-061989-071989-081989-091989-101989-111989-121990-011990-021990-031990-041990-051990-061990-071990-081990-091990-101990-111990-121991-011991-021991-031991-041991-051991-061991-071991-081991-091991-101991-111991-121992-011992-021992-031992-041992-051992-061992-071992-081992-091992-101992-111992-121993-011993-021993-031993-041993-051993-061993-071993-081993-091993-101993-111993-121994-011994-021994-031994-041994-051994-061994-071994-081994-091994-101994-111994-121995-011995-021995-031995-041995-051995-061995-071995-081995-091995-101995-111995-121996-011996-021996-031996-041996-051996-061996-071996-081996-091996-101996-111996-121997-011997-021997-031997-041997-051997-061997-071997-081997-091997-101997-111997-121998-011998-021998-031998-041998-051998-061998-071998-081998-091998-101998-111998-121999-011999-021999-031999-041999-051999-061999-071999-081999-091999-101999-111999-122000-012000-022000-032000-042000-052000-062000-072000-082000-092000-102000-112000-122001-012001-022001-032001-042001-052001-062001-072001-082001-092001-102001-112001-122002-012002-022002-032002-042002-052002-062002-072002-082002-092002-102002-112002-122003-012003-022003-032003-042003-052003-062003-072003-082003-092003-102003-112003-122004-012004-022004-032004-042004-052004-062004-072004-082004-092004-102004-112004-122005-012005-022005-032005-042005-052005-062005-072005-082005-092005-102005-112005-122006-012006-022006-032006-042006-052006-062006-072006-082006-092006-102006-112006-122007-012007-022007-032007-042007-052007-062007-072007-082007-092007-102007-112007-122008-012008-022008-032008-042008-052008-062008-072008-082008-092008-102008-112008-122009-012009-022009-032009-042009-052009-062009-072009-082009-092009-102009-112009-122010-012010-022010-032010-042010-052010-062010-072010-082010-092010-102010-112010-122011-012011-022011-032011-042011-052011-062011-072011-082011-092011-102011-112011-122012-012012-022012-032012-042012-052012-062012-072012-082012-092012-102012-112012-122013-012013-022013-032013-042013-052013-062013-072013-082013-092013-102013-112013-122014-012014-022014-032014-042014-052014-062014-072014-082014-092014-102014-112014-122015-012015-022015-032015-042015-052015-062015-072015-082015-092015-102015-112015-122016-012016-022016-032016-042016-052016-062016-072016-082016-092016-102016-112016-122017-012017-022017-032017-042017-052017-062017-072017-082017-092017-102017-112017-122018-012018-022018-032018-042018-052018-062018-072018-082018-092018-102018-112018-122019-012019-022019-032019-042019-052019-062019-072019-082019-092019-102019-112019-122020-012020-022020-032020-042020-052020-062020-072020-082020-092020-102020-112020-122021-012021-022021-032021-042021-052021-062021-072021-082021-092021-102021-112021-122022-012022-022022-032022-042022-052022-062022-072022-082022-092022-102022-112022-122023-012023-022023-032023-042023-052023-062023-072023-082023-092023-102023-112023-122024-012024-022024-032024-042024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-042025-052025-062025-072025-082025-092025-102025-112025-122026-012026-022026-032026-04
seasonseason

Rolling 3-month window named by the initials of its months (JFM = Jan-Mar ... DJF = Dec-Feb). Each value covers three months; NDJ and DJF span the year boundary and are labelled by the year in which they end.

JFMFMAMAMAMJMJJJJAJASASOSONONDNDJDJF
yeartemporal

Year in which a seasonal window ends; the {year} token of the seasonal asset.

1981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017201820192020202120222023202420252026

Access & use

Cloud-optimised distributions — read just the window you need directly from object storage.

Machine-readable file index: STAC Collection

Data

monthly5 MB

Per-pixel monthly precipitation totals, one COG per month.

Format: Cloud-Optimized GeoTIFF — best for single-file downloads, visualisation, and traditional GIS tools

Access a specific fileDownload

https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=PTOT/PTOT-1981-01.tif

Other access methods

File pattern: variable={variable}/{variable}-{time}.tif

HTTPS directory:https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/
S3 directory:s3://digital-atlas/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/

monthly — usage examples

Python
import rioxarray

url = "https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=PTOT/PTOT-1981-01.tif"
da = rioxarray.open_rasterio(url, masked=True)

# Clip a window (East Africa)
window = da.rio.clip_box(
    minx=28, miny=-12, maxx=52, maxy=18
)

# ...or sample a single point
point = da.sel(x=36.8, y=-1.3, method="nearest")
seasonal

Per-year rolling 3-month precipitation sums, one COG per season x year.

Format: Cloud-Optimized GeoTIFF — best for single-file downloads, visualisation, and traditional GIS tools

Access a specific fileDownload

https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=seasonal/variable=PTOT/season=JFM/PTOT_JFM_1981_sum.tif

Other access methods

File pattern: variable={variable}/season={season}/{variable}_{season}_{year}_sum.tif

HTTPS directory:https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=seasonal/
S3 directory:s3://digital-atlas/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=seasonal/

seasonal — usage examples

Python
import rioxarray

url = "https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=seasonal/variable=PTOT/season=JFM/PTOT_JFM_1981_sum.tif"
da = rioxarray.open_rasterio(url, masked=True)

# Clip a window (East Africa)
window = da.rio.clip_box(
    minx=28, miny=-12, maxx=52, maxy=18
)

# ...or sample a single point
point = da.sel(x=36.8, y=-1.3, method="nearest")

Technical metadata

The full spatial, temporal, and modelling detail behind the summaries elsewhere on this page.

Spatial

CRS
EPSG:4326
Extent (W S E N)
[-20, -40, 55, 40]
Coverage
Africa
Resolution (xy)
0.05 deg (~5.5 km, CHIRPS native)

Temporal

Start
1981-01
End
open (present)

Record

Resource type
dataset
Created
2026-08-28
Updated
2026-09-14

Provenance

Ordered processing steps, from source to publication.

  1. source

    CHIRPS v3.0 quasi-global daily/monthly precipitation (station + infrared blend).

    Derived from: CHIRPS-3.0 data portal (Climate Hazards Center), CDH catalog record chirps-v3-daily

  2. monthly-cog

    Monthly PTOT over the Africa window, written as COGs with internal overviews.

    code: https://github.com/AdaptationAtlas/hazards_prototype @ R/observational

  3. seasonal-sum

    Per-year tri-month sums over the twelve seasonal windows, written as COGs with overviews.

    code: https://github.com/AdaptationAtlas/hazards_prototype @ R/observational/5b_make_obs_seasonal_rasters.R

How to cite

Please cite the dataset and any associated peer-reviewed publications.

Steward, P., Funk, C., Peterson, P., Landsfeld, M. (2026). Monthly and seasonal precipitation from CHIRPS v3 (Africa), Africa Agriculture Adaptation Atlas [Data set]. Alliance of Bioversity International and CIAT. Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/africa-precipitation-monthly-seasonal/.

Related publications

  • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., Michaelsen, J. (2015). The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes. Scientific Data. doi:10.1038/sdata.2015.66

Contacts

Who produced this dataset, and who to reach about it.

Climate Hazards Center, UC Santa BarbaraLicensor
Producer
Peter Steward
Alliance of Bioversity International and CIAT
ProcessorPoint of contact