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Data

Precipitation anomalies

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Climate Change

What you should know about this indicator

How is this data described by its producer?

This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.

Precipitation anomalies
The difference in a specific year's total precipitation—rain and snow—from the 1991–2020 average, measured in millimeters, excluding fog and dew.
Source
Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data
Last updated
January 16, 2026
Next expected update
January 2027
Date range
1940–2025
Unit
millimeters

Sources and processing

This data is based on the following sources

Contains modified Copernicus Climate Change Service information – ERA5 monthly averaged data on single levels from 1940 to present

Monthly averages of total precipitation from the ERA5 reanalysis. The data is on single levels and covers the period from 1940 to present. The data is available at a spatial resolution of 0.25 degrees. The data is provided by the Copernicus Climate Change Service (C3S) Climate Data Store (CDS).

Retrieved on
January 16, 2026
Retrieved from
https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 (Accessed on 16-Jan-2026)

Monthly averages of total precipitation from the ERA5 reanalysis. The data is on single levels and covers the period from 1940 to present. The data is available at a spatial resolution of 0.25 degrees. The data is provided by the Copernicus Climate Change Service (C3S) Climate Data Store (CDS).

Retrieved on
January 16, 2026
Retrieved from
https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 (Accessed on 16-Jan-2026)

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator
  • Initially, the dataset is provided with specific coordinates in terms of longitude and latitude. To tailor this data to each country, we use geographical boundaries as defined by the World Bank. The method involves trimming the precipitation dataset to match the exact geographical shape of each country. To correct for potential distortions caused by projecting the Earth's curved surface onto a flat map, we apply a latitude-based weighting. This step is essential for maintaining accuracy, particularly in high-latitude regions where distortion is more pronounced. The result of this process is a latitude-weighted average precipitation for each nation.
  • It’s important to note, however, that due to the resolution constraints of the Copernicus dataset, this methodology might not be as effective for countries with very small landmasses. In such cases, the process may not yield reliable data.
  • The derived precipitation for each country is calculated based on administrative borders, encompassing all land surface types within these areas. As a result, precipitation over oceans and seas is not included in these averages, keeping the data focused on terrestrial environments.
  • Global precipitation averages and anomalies, however, are calculated over both land and ocean surfaces.
  • The precipitation anomaly is calculated by comparing the average precipitation of a specific time period (e.g., a particular year or month) to the average surface precipitation of the same period from 1991 to 2020.
  • When calculating anomalies for each country, the total precipitation of a given year or month is compared to the 1991-2020 average precipitation for that specific country.
  • The reason for using the 1991-2020 period as the reference mean is that it is the standard reference period used by our data source, the Copernicus Climate Change Service. This period is also adopted by the UK Met Office. This approach ensures consistency in identifying climate variations over time.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Precipitation anomalies”, part of the following publication: Hannah Ritchie, Pablo Rosado, and Veronika Samborska (2024) - “Climate Change”. Data adapted from Contains modified Copernicus Climate Change Service information. Retrieved from https://archive.ourworldindata.org/20260324-093454/grapher/global-precipitation-anomaly.html [online resource] (archived on March 24, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data

Full citation

Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data. “Precipitation anomalies” [dataset]. Contains modified Copernicus Climate Change Service information, “ERA5 monthly averaged data on single levels from 1940 to present 2” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260324-093454/grapher/global-precipitation-anomaly.html (archived on March 24, 2026).

Download

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/global-precipitation-anomaly.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/global-precipitation-anomaly.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/global-precipitation-anomaly.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/global-precipitation-anomaly.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/global-precipitation-anomaly.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/global-precipitation-anomaly.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/global-precipitation-anomaly.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/global-precipitation-anomaly.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear