Abstract Global and large regional rainfall variations and possible long-term changes are examined using the 27-yr (1979–2005) Global Precipitation Climatology Project (GPCP) monthly dataset. Emphasis is placed on discriminating among variations due to ENSO, volcanic events, and possible long-term climate changes in the Tropics. Although the global linear change of precipitation in the dataset is near zero during the time period, an increase in tropical rainfall is noted in the dataset, with a weaker decrease over Northern Hemisphere middle latitudes. Focusing on the Tropics (25°S–25°N), the dataset indicates an upward linear change (0.06 mm day−1 decade−1) and a downward linear change (−0.01 mm day−1 decade−1) over tropical ocean and land, respectively. This corresponds to an about 5.5% increase (ocean) and 1% decrease (land) during the entire 27-yr time period. The year 2005 has the largest annual tropical total precipitation (land plus ocean) for the GPCP record. The five highest years are (in descending order) 2005, 2004, 1998, 2003, and 2002. For tropical ocean the five highest years are 1998, 2004, 2005, 2002, and 2003. Techniques are applied to isolate and quantify variations due to ENSO and two major volcanic eruptions during the time period (El Chichón, March 1982; Mount Pinatubo, June 1991) in order to examine longer-time-scale changes. The ENSO events generally do not impact the tropical total rainfall, but rather induce significant anomalies with opposite signs over tropical land and ocean. The impact of the two volcanic eruptions is estimated to be about a 5% reduction in tropical rainfall over both land and ocean. A modified dataset (with ENSO and volcano effects removed) retains the same approximate linear change slopes, but with reduced variances, thereby increasing the statistical significance levels associated with the long-term rainfall changes in the Tropics. However, although care has been taken to ensure that this dataset is as homogeneous as possible, firm establishment of the existence of the discussed changes as long-term trends may require continued analysis of the input datasets and a lengthening of the observation period.