CHIRPS v3 Daily Reanalysis Rainfall – ERA5-Based

Short Description

CHIRPS v3 Daily Reanalysis is a quasi-global daily rainfall dataset produced by the Climate Hazards Center at UC Santa Barbara. It combines satellite-based thermal infrared rainfall estimates, rainfall-station observations and high-resolution climatology. The daily reanalysis product uses ERA5 rainfall patterns to distribute CHIRPS pentadal precipitation totals into daily rainfall amounts.

Dataset Overview

Climate Hazards Center Infrared Precipitation with Stations Version 3, commonly known as CHIRPS v3, is a long-term high-resolution rainfall dataset designed for climate, drought, agricultural, hydrological and environmental analysis.

The dataset covers land areas between approximately 60°S and 60°N across all longitudes. It provides rainfall information from 1981 to near-present at a spatial resolution of 0.05 degrees, approximately 5.6 kilometres near the equator.

CHIRPS v3 combines satellite thermal infrared observations with ground-based rainfall-station measurements and an improved rainfall climatology. The Version 3 production system includes more rainfall-station sources than Version 2 and introduces improvements intended to represent rainfall variability and intense rainfall more effectively.

CHIRPS is fundamentally produced at pentadal and monthly time steps. The Daily Reanalysis product uses daily precipitation patterns from the ECMWF ERA5 reanalysis to distribute each pentadal CHIRPS rainfall total into daily values. The resulting daily data preserve the corresponding pentadal precipitation accumulation.

The dataset can support rainfall-runoff modelling, drought analysis, watershed studies, agricultural monitoring, climate variability studies, rainfall trend analysis and research in areas where conventional rain-gauge records are incomplete or unavailable.

Users should still compare the dataset with reliable local observations whenever possible, particularly in mountainous terrain, small catchments and locations affected by highly localised convective rainfall.

Variables and Data Contents

Daily precipitation depth expressed in millimetres per day.

Main variable:
precipitation – daily rainfall amount in mm/day

Supporting image properties include:
year
month
day

The daily reanalysis values are derived by using ERA5 daily rainfall patterns to partition CHIRPS v3 pentadal precipitation totals.

Strengths

CHIRPS v3 provides a continuous rainfall record extending from 1981 to near-present, making it useful for long-term hydrological, agricultural and climate analysis.

The 0.05-degree grid offers substantially better spatial detail than many global climate and reanalysis rainfall products.

The dataset combines satellite rainfall information with ground-station observations instead of relying on only one data source.

Its consistent global grid is useful for regional studies, transboundary basins and locations where national rainfall records are incomplete, inaccessible or spatially sparse.

Multiple time steps and file formats are available, including daily, pentadal, dekadal, monthly and annual data.

The dataset can be accessed directly from the CHC repository or processed at large scale using Google Earth Engine.

Version 3 includes more station-observation sources and an improved climatology and rainfall-estimation method.

Limitations and Quality Considerations

CHIRPS is a gridded rainfall estimate and should not automatically be treated as a replacement for reliable local rain-gauge observations.

The approximate 5.6 km grid may not capture highly localised rainfall, especially convective storms over small urban or mountainous catchments.

The Daily Reanalysis product is derived from pentadal CHIRPS totals. ERA5 daily rainfall patterns are used to distribute the pentadal total into individual days. Daily values are therefore not produced through direct daily station blending.

The data cover land between 60°S and 60°N and do not provide complete polar coverage.

Rainfall estimates can contain regional biases associated with terrain, rainfall type, station density and satellite-observation limitations.

Local validation, bias assessment and, where necessary, bias correction are recommended before calibration of important engineering models.

Daily CHIRPS data are generally unsuitable as the only precipitation source for short-duration urban flash-flood studies that require hourly or sub-hourly rainfall.

Recommended Uses

Long-term rainfall analysis
Rainfall-runoff modelling
Watershed and basin studies
Drought monitoring
Seasonal rainfall assessment
Rainfall anomaly analysis
Agricultural drought analysis
Crop and irrigation studies
Climate variability and trend analysis
Water-balance studies
Hydrological modelling in data-scarce regions
Comparison and validation of other rainfall products
Preparation of basin-average rainfall series
Research on rainfall impacts on vegetation, forests and land degradation

Use with SWAT+

Long-term rainfall analysis
Rainfall-runoff modelling
Watershed and basin studies
Drought monitoring
Seasonal rainfall assessment
Rainfall anomaly analysis
Agricultural drought analysis
Crop and irrigation studies
Climate variability and trend analysis
Water-balance studies
Hydrological modelling in data-scarce regions
Comparison and validation of other rainfall products
Preparation of basin-average rainfall series
Research on rainfall impacts on vegetation, forests and land degradation

Use with HEC-HMS

CHIRPS v3 can support continuous and long-duration rainfall-runoff simulation in HEC-HMS.

The data can be processed into basin-average rainfall, subbasin rainfall or a gridded precipitation input. GIS, Python or R can be used to clip the rainfall grids, calculate watershed averages and export the resulting time series.

For continuous simulation, daily rainfall can support soil-moisture accounting, water-balance studies and long-term runoff analysis.

For event-based flood simulation, the daily temporal resolution may be too coarse. Hourly or sub-hourly rainfall products, radar rainfall or local gauge data should be preferred for short-duration flood events.

Before HEC-HMS calibration, rainfall totals should be compared with available local observations and adjusted when a systematic bias is present.

Use with HEC-RAS

CHIRPS rainfall can be combined with NDVI, EVI, leaf-area index, evapotranspiration, soil moisture and crop-condition datasets.

Possible applications include:

Rainfall-productivity relationships
Crop-season rainfall totals
Rainfall onset and cessation analysis
Agricultural drought monitoring
Vegetation response to rainfall anomalies
Rain-use efficiency
Biomass and crop-yield modelling
Rangeland condition assessment
Identification of rainfall-limited plant production

Vegetation and Plant-Production Analysis

CHIRPS can provide rainfall context for forest-health, canopy-condition, fire-risk and drought-impact studies.

It may be combined with forest-cover, tree-loss, vegetation-index, burned-area, land-surface-temperature and soil-moisture products to examine:

Forest drought stress
Rainfall deficits preceding fires
Post-fire vegetation recovery
Seasonal forest greenness
Rainfall relationships with forest loss or degradation
Long-term hydroclimatic pressure on forest ecosystems

Forest Monitoring

CHIRPS can provide rainfall context for forest-health, canopy-condition, fire-risk and drought-impact studies.

It may be combined with forest-cover, tree-loss, vegetation-index, burned-area, land-surface-temperature and soil-moisture products to examine:

Forest drought stress
Rainfall deficits preceding fires
Post-fire vegetation recovery
Seasonal forest greenness
Rainfall relationships with forest loss or degradation
Long-term hydroclimatic pressure on forest ecosystems

Desertification and Land-Degradation Analysis

CHIRPS can support analysis of rainfall variability, prolonged rainfall deficits, drought frequency and rainfall trends associated with land degradation and desertification.

It can be combined with vegetation indices, bare-soil indicators, soil erosion, land-cover change, soil moisture and evapotranspiration data.

Possible applications include:

Rainfall-anomaly mapping
Drought-frequency analysis
Rainfall erosivity studies
Assessment of vegetation decline during dry periods
Identification of persistent rainfall-deficit zones
Evaluation of restoration-project rainfall conditions
Climate context for land-degradation neutrality indicators

Download and Processing Guidance

1. Open the official CHIRPS v3 data repository.
2. Select the daily folder and identify the Daily Reanalysis or RNL product.
3. Select the required format, such as GeoTIFF, NetCDF, BIL or Cloud-Optimised GeoTIFF.
4. Select the required geographic domain and year.
5. Download only the years required for the analysis to reduce processing time and storage.
6. Open the files in QGIS, ArcGIS, Python, R, GDAL, GRASS GIS or another suitable geospatial tool.
7. Clip the rainfall grids to the watershed, country or study area.
8. Confirm the coordinate reference system and rainfall units.
9. Aggregate or extract rainfall values according to the modelling requirement.
10. Export watershed-average, subbasin-average or virtual-station time series.
11. Check dates, leap years, missing values and unit consistency.
12. Compare the extracted rainfall with available local rain-gauge observations.
13. Apply bias correction where justified.
14. Convert the processed series to the input structure required by SWAT+, HEC-HMS or another hydrological model.
15. Record the dataset version, download date, daily-product type and processing method in the project documentation.

Google Earth Engine users can access the collection with:

ee.ImageCollection("UCSB-CHC/CHIRPS/V3/DAILY_RNL")

Researchers using Python may access Earth Engine through the Earth Engine Python API or may process downloaded GeoTIFF and NetCDF files with packages such as xarray, rasterio, rioxarray, geopandas and pandas.

R users may process the data with packages such as terra, stars, sf, ncdf4 and exactextractr.