Gauge-based global gridded precipitation products often have two problems: (1) there can be large systematic biases in precipitation measurement, especially for solid precipitation, and (2) precipitation is often underestimated in topographically complex regions due to the prevalence of low elevation valley stations. Precipitation is the main driver of the land surface hydrologic system and therefore the most important input variable to hydrology models. We describe an approach we have taken to correct global gridded precipitation data for the two above-mentioned sources of bias. The final product is a (1979 - 1999) gridded precipitation climatology for the global land areas that is adjusted for systematic biases on a monthly basis and for orographic effects on an annual basis. Both adjustments are designed to be applied to the existing 0.5° precipitation product developed by Cort Willmott and others at the University of Delaware. Adjustments for wind-induced under-catch of solid precipitation were estimated using gauge type-specific regression equations from the recent (1998) World Meteorological Organization Solid Precipitation Measurement Intercomparison. Wind-induced undercatch of liquid precipitation and wetting losses were estimated using the methods employed in previous global bias adjustment efforts. In an attempt to develop a globally consistent correction for the underestimation of gridded precipitation in mountainous regions, we used a hydrologic water balance approach. The precipitation in orographically-influenced drainage basins was adjusted using a combination of water balance and variations of the Budyko ET/P vs. PET/P curve. The method is similar to other methods in which streamflow measurements are distributed back onto the watershed and a water balance is performed to determine “true” precipitation. Rather than relying on modeled runoff ratios, we estimated evaporation using Budyko ET/P vs. PET/P curves. Combination of the gauge catch deficiency and orographic adjustments resulted in a net increase of 17.5% of estimated global terrestrial mean annual precipitation (11.7% and 5.8%, respectively). We also estimated the effects of the adjustments on mean annual and monthly precipitation for large continental-scale river basins. In general, river basins with considerable orography (e.g. the Brahmaputra, Columbia, and Yukon) experienced the greatest precipitation increases due to correction for orographic effects, while river basins in colder climates (e.g. the Lena, Ob, and Yukon) experienced the greatest precipitation increases due to adjustment for systematic bias (especially in the winter).