UCSB CHC CHIRPS analysis, final
| Spatial domain | Global land, 60 degrees north to 60 degrees south |
| Spatial resolution | 0.05 degrees (~5km) |
| Time domain | 1981-01-01 00:00:00 UTC to Present |
| Time resolution | 1 day |
STAC (browse) · validation report
The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) version 3 combines satellite estimates and station observations into a precipitation record over land.
Daily CHIRPS version 3 precipitation over land from 60 degrees south to 60 degrees north, at 0.05 degree resolution, from 1981 onward. This is the final product, using the reanalysis-based daily distribution (final/rnl).
Related Datasets
Examples
|
CHIRPS preliminary and final (Github) |
|
CHIRPS preliminary and final (Colab) |
import dynamical_catalog # dynamical-catalog>=1.0.0
import os
os.environ["DYNAMICAL_STAC_CATALOG_URL"] = "https://stac-staging.dynamical.org/catalog.json"
ds = dynamical_catalog.open("ucsb-chc-chirps-analysis-final", chunks=None)
ds["precipitation_surface"].sel(time="2025-01-01", latitude=-3, longitude=-60, method="nearest")import icechunk
import pystac
import xarray as xr
catalog = pystac.Catalog.from_file("https://stac-staging.dynamical.org/catalog.json")
collection = catalog.get_child("ucsb-chc-chirps-analysis-final")
asset = collection.assets["icechunk-https"]
repo = icechunk.Repository.open(icechunk.http_storage(asset.href))
session = repo.readonly_session("main")
import os
os.environ["DYNAMICAL_STAC_CATALOG_URL"] = "https://stac-staging.dynamical.org/catalog.json"
ds = xr.open_zarr(session.store, chunks=None)
ds["precipitation_surface"].sel(time="2025-01-01", latitude=-3, longitude=-60, method="nearest")Dimensions
| min | max | units | |
|---|---|---|---|
| latitude | -59.975 | 59.975 | degree_north |
| longitude | -179.975 | 179.975 | degree_east |
| time | 1981-01-01T00:00:00Z | Present | seconds since 1970-01-01 |
Variables
Access a variable by its name (the bold identifier, e.g. ds["precipitation_surface"]).
Dimensions: time × latitude × longitude
| variable | units |
|---|---|
precipitation_surface
Precipitation rate (prate)Average precipitation rate over the 24 hours starting at the time coordinate. Units equivalent to mm/s. NaN over oceans.
|
kg m-2 s-1 |
Details
License
Dataset licensed under CC BY 4.0.
Attribution and citation
UCSB Climate Hazards Center CHIRPS version 3.0 data processed by dynamical.org from the Climate Hazards Center data archive. Funk, C., Peterson, P., Harrison, L. et al. The Climate Hazards Center Infrared Precipitation with Stations, Version 3. Sci Data 13, 718 (2026), data https://doi.org/10.15780/G2JQ0P.
Or UCSB CHC CHIRPS from dynamical.org.
Product
Final is released monthly, usually two to three weeks after the end of the month. It uses the best available rain-gauge reports with more quality checks than preliminary. ERA5 rainfall is used to split five-day CHIRPS totals into daily amounts.
Source
The CHIRPS v3 documentation describes the products and release schedule. Source files are provided by the UCSB Climate Hazards Center daily archive.
Storage
Storage for this dataset is generously provided by AWS Open Data.
Chunks and shards
This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. Chunks are grouped into larger shards (the objects actually written to storage), which keeps the object count manageable for long-archive datasets. When possible, aligning your reads with this dataset's chunk grid can significantly improve data access speed.
The element count and coordinate span of this dataset:
| dimension | chunk | shard |
|---|---|---|
| time | 365 (365 days) | 365 (365 days) |
| latitude | 60 (3°) | 1200 (60°) |
| longitude | 60 (3°) | 1800 (~90°) |
| uncompressed | 5.0 MiB | 2.9 GiB |
Validation report
Review the validation report for variable availability, missing data, known quirks, fill values, value distributions, and sample plots.
Compression
The data values in this dataset have been rounded in their binary floating point representation to improve compression. See Klöwer et al. 2021 for more information on this approach. The exact number of rounded bits can be found in our reformatting code.