Africa - GPCC Monthly Precipitation Product

The Combined Drought Indicator (CDI) dataset developed at ACMAD is designed as an operational drought-monitoring product that synthesizes multiple biophysical signals into a single, coherent assessment of drought conditions across Africa. Rather than relying on a single indicator, the ACMAD CDI combines information from rainfall-based drought indices and satellite-derived vegetation condition metrics, applying rule-based logic to identify areas where these independent indicators consistently point to drought stress. This convergence approach reduces the risk of false alarms that can arise from short-term rainfall variability or vegetation anomalies unrelated to drought. The data exist in two forms one based on GPCC while another based on Era5 precipitation. The dataset is particularly useful in the African context, where ground observations are sparse and climatic gradients are strong across regions. By using harmonized, gridded datasets, the CDI provides a comparable drought signal across national boundaries, supporting regional coordination and continental-scale analysis. Its categorical outputs are intentionally simple and policy-ready, making them suitable for drought bulletins, early-warning communications, and high-level decision support. Importantly, the CDI is not intended to replace detailed drought diagnostics or impact assessments. Instead, it serves as a convergence and confirmation tool that highlights areas requiring closer analysis and expert interpretation. In this role, the CDI acts as a bridge between complex climate data and practical drought-risk management, enabling timely, consistent, and defensible drought monitoring at regional and continental scales.

The Standardized Precipitation Index (SPI) is the globally recommended standard for characterizing meteorological drought. It quantifies observed precipitation as a standardized deviation from the long-term probability distribution function for a specific location and time scale. This dataset typically includes multiple time-scales (e.g., 1-month, 3-month, 6-month, 12-month) to capture different types of drought impacts, from immediate soil moisture deficits to long-term hydrological stress. Because SPI is normalized, it allows for safe comparisons between different climatic zones—allowing a wet region and a dry region to be compared on the same scale of drought severity.

This dataset provides annual cumulative precipitation derived from monthly land-surface precipitation from the Global Global Precipitation Climatology Centre (GPCC) dataset. The GPCC data comes from rain-gauges built on GTS-based and historical data from January 1891 through December 2019, with ~85,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~15,000 (before 6000) to more than 50,000 stations. The data is available globally at 0.25 × 0.25 degree resolution. The Global Precipitation Climatology Centre (GPCC) is operated by Deutscher Wetterdienst (DWD) under the auspices of the World Meteorological Organization (WMO). Annual cumulative precipitation

The Soil Moisture Anomaly (SMA) dataset provides a measure of deviation in root-zone soil moisture conditions relative to the long-term climatological mean. Generated typically from hydrological models or satellite assimilation systems, this product highlights deficits or surpluses in soil water availability that are critical for agriculture and pastoralism. Because soil moisture integrates rainfall over time and accounts for evapotranspiration losses, SMA offers a more direct indicator of agricultural drought stress than precipitation deficits alone. This dataset is essential for monitoring the onset of agricultural drought, as soil moisture deficits often precede visible vegetation stress. It is widely used to assess potential yield reductions in rainfed agriculture and to monitor fodder availability in pastoral regions.

This version of the Combined Drought Indicator (CDI) utilizes ERA5 reanalysis data as the primary input for the precipitation component (SPI), rather than gauge-only or other satellite-blended products. ERA5, produced by ECMWF, offers comprehensive spatial coverage and temporal consistency, making it valuable for regions where gauge networks are failing or sparse. Like the standard CDI, this dataset synthesizes rainfall deficits (via ERA5), soil moisture anomalies, and vegetation stress into a single classification system to monitor the evolution of drought episodes.

Precipitation data is delivered on a daily basis. The source of this dataset is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) quasi-global rainfall dataset, starting from 1981 up to near present. The value of each pixel represents the total of daily precipitation in the month expressed in mm (1mm=1l/m² or 1mm=10m³/ha). The data is provided from January 2009 to 2024. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm (1mm=1l/m² or 1mm=10m³/ha) Map code : L1-PCP-M Scale Factor : 0.1 Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See daily Precipitation (L1_PCP_E) for further information. The monthly data is obtained by taking the daily PCP, summing the days of each month. **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage