Overview
Standardized Precipitation-Evapotranspiration Index (SPEI) at 3-month and 12-month accumulation, as per-pixel monthly Cloud Optimized GeoTIFFs over Africa, 1981 to present. The index is computed from the climatic water balance (CHIRPS v3 precipitation minus Hargreaves potential evapotranspiration from CHIRTS-ERA5 temperature) on the 0.05° CHIRPS grid, fitted to a log-logistic distribution over the 1991-2020 reference period. SPEI-03 tracks seasonal and agricultural drought; SPEI-12 tracks hydrological and multi-year drought.
Important note
Files for the first months of each accumulation window exist but hold no data (1981-01 to 1981-02 for SPEI-03, 1981-01 to 1981-11 for SPEI-12). In arid cells the log-logistic fit is poorly constrained: non-finite results are stored as NoData, and magnitudes well beyond ±3 can occur where the fit is degenerate rather than indicating record drought or wetness.
Quick start
import rioxarray
url = "https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=SPEI-03/SPEI-03-1981-01.tif"
da = rioxarray.open_rasterio(url, masked=True)library(terra)
url <- "/vsicurl/https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=SPEI-03/SPEI-03-1981-01.tif"
r <- rast(url)Intended uses
- seasonal (SPEI-03) and multi-year (SPEI-12) drought monitoring over Africa
- comparing drought conditions across ENSO phases or analogue years
- drought context for vegetation, crop water-stress and food security indicators
Not recommended for
Why: SPEI is a standardized anomaly against the 1991-2020 fitted distribution, not a quantity of water.
Use instead: Monthly and seasonal precipitation totals (catalog record africa-precipitation-monthly-seasonal).
Why: Values depend on the input data, the PET method and the reference period, which differ between products.
Access & use
Files, endpoints, and worked examples for each distribution.
Data
Per-pixel monthly SPEI, one COG per variable per month.
Format: Cloud-Optimized GeoTIFF — best for single-file downloads, visualisation, and traditional GIS tools
List of files 1,088 filesDownload CSV
Individual file
https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=SPEI-03/SPEI-03-1981-01.tif
Usage examples
import rioxarray
url = "https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=SPEI-03/SPEI-03-1981-01.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")library(terra)
url <- "/vsicurl/https://digital-atlas.s3.amazonaws.com/domain=climate/type=observational/source=chirps-chirts-era5/region=africa/processing=monthly/variable=SPEI-03/SPEI-03-1981-01.tif"
r <- rast(url)
# Clip a window (East Africa)
window <- crop(r, ext(28, 52, -12, 18))
# ...or sample a single point
point <- extract(r, cbind(36.8, -1.3))Other access methods
File pattern: variable={variable}/{variable}-{time}.tif
Data dictionary
What each file holds. A layout describes how the data is stored in a file: its variables, the dimensions they vary over, and any keys.
File layouts
| Files | Format | Variables | Dimensions | No data |
|---|---|---|---|---|
| monthly | Cloud-Optimized GeoTIFF | SPEI-03, SPEI-12 | cell × time | NaN |
Variables
| Variable | Type | Unit | Description |
|---|---|---|---|
| SPEI-03 | float32 | 1 | SPEI over the 3 months ending in the labelled month: the standardized climatic water balance (precipitation minus potential evapotranspiration) relative to the 1991-2020 distribution for that calendar month. Zero is the reference median; negative is drier than the reference climatology, positive wetter. Values below -1 indicate moderate and below -2 extreme drought. |
| SPEI-12 | float32 | 1 | SPEI over the 12 months ending in the labelled month, standardized as for SPEI-03. The longer window responds to sustained deficits in soil moisture, streamflow and storage. |
Dimensions
0.05 deg (~5.5 km, CHIRPS native).
Grid spacing: 0.05 degree
Last month of the accumulation window (YYYY-MM); the {time} token of the monthly asset.
Range: 1981-01 – 2026-04, monthly
Technical metadata
The full spatial, temporal, and modeling detail behind the summaries elsewhere on this page.
Spatial
- CRS
- EPSG:4326
- Extent (W S E N)
- [-20, -40, 55, 40]
- Coverage
- Africa
- Grid spacing
- 3 arc-minutes (~5.6 km at the equator)
Temporal
- Start
- 1981-01
- End
- open (present)
Climate methods
- Baseline
- 1991 – 2020
Provenance
Ordered processing steps, from source to publication.
- source
Monthly CHIRPS v3 precipitation and CHIRTS-ERA5 monthly maximum and minimum temperature, cut to the Africa window.
- spei
Monthly potential evapotranspiration by Hargreaves-Samani from Tmax, Tmin, their mean and extraterrestrial radiation at mid-month; climatic water balance = precipitation minus PET. Per-pixel SPEI at 3- and 12-month scales with the SPEI R package, log-logistic fit on 1991-2020. Pixels with fewer than scale + 24 valid months and non-finite fits set to NoData. Written as COGs with internal overviews.
How to cite
Please cite the dataset and any associated peer-reviewed publications.
P. Steward (2026) 3- and 12-month SPEI from CHIRPS v3 and CHIRTS-ERA5 (Africa). Version 1. Alliance of Bioversity International and CIAT. Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/africa-spei-drought_1/.
BibTeX
@misc{africa_spei_drought_2026,
title = {3- and 12-month SPEI from CHIRPS v3 and CHIRTS-ERA5 (Africa)},
author = {Steward, P.},
year = {2026},
version = {1},
publisher = {Alliance of Bioversity International and CIAT},
note = {Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/africa-spei-drought_1/}
}Attribution
Reproduce this credit line wherever you reuse the data.
Derived from CHIRPS v3 and CHIRTS-ERA5, Climate Hazards Center, UC Santa Barbara.
Contacts
Who produced this dataset, and who to reach about it.