This dataset includes the Standardized Precipitation Index (SPI) at three-, six-, and 12-month scales for global land surfaces. It was produced for a study to determine the relationship between climate variability and armed conflict in Sub-Saharan Africa (O'Loughlin et al. 2012). The precipitation data (1949-2012) are resampled from the original University of East Anglia Climate Research Unit (CRU) global time series, TS3.21, monthly 0.5 degree by 0.5 degree grids to the study unit of analysis, 1.0 degree by 1.0 degree grids, thereby facilitating regression with environmental and socioeconomic variables.

The Standardized Precipitation Index (SPI) is commonly used to monitor drought and anomalous wet periods. It was formulated by Tom McKee, Nolan Doesken, and John Kleist of the Colorado Climate Center, Colorado State University (McKee et al. 1993). The SPI at a given location is based only on the long-term precipitation record for a desired period. The long-term precipitation time series is fitted to a gamma probability distribution, which is then transformed into a normal distribution so that the mean SPI is zero. Theoretically, the SPI is the number of standard deviations by which the observed value would lie above or below the long-term mean, for a normally distributed random variable. Thus, the index can be used to compare precipitation across a region with different climates. The SPI can be calculated for multiple time scales, which allows assessment of impacts on different water resources. For example, soil moisture responds to precipitation departures on a short time scale, while stream flow responds to anomalies on a longer time scale. Precipitation amounts that indicate wet conditions at one time scale could indicate dry conditions at another time scale.