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Uncertainty in calculating the spatial-and interannual variability of precipitation over India and Tibet from widely used gridded precipitation datasets is examined for the 29-year period from 1979 to 2007. Uncertainty is defined in terms of the spread among the variability calculated from multiple datasets, a useful method when multiple datasets of similar or unknown accuracy are available for analyses. The resulting uncertainty varies for regions and seasons. Geographical variations are clearly seen in the signal-to-noise ratio (SNR), with the largest uncertainty in the Jammu and Kashmir (J&K), western India-eastern Pakistan, Tibet, Hindu-Kush mountains, and Western Ghats which are characterized by either dry climate or complex terrain, or both. Seasonally, the uncertainty is larger for the December-February period (DJF) than for the June-September period (JJAS) in most of the region except J&K which is characterized by two wet seasons: winter and summer. The uncertainty in the interannual variability also varies according to regions especially in J&K where the calculated interannual variability varies by nearly a factor of two among the datasets. The uncertainty range in the calculated interannual variability is determined largely by two gauge-based data of the finest resolution, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of water resources (smallest) and India Meteorological Department (largest) in all regions.

The regional and seasonal variations in the uncertainty do not appear to depend on either the spatial resolution or the length of records. This implies that analysis methodology such as the quality control of input data, spatial/temporal interpolation, and retrieval algorithms used in producing these gridded datasets plays a crucial role in determining the characteristics of precipitation climatology represented by individual datasets. Our results show that calculating precipitation characteristics must be accompanied by careful examinations of uncertainty among available datasets, especially for dry seasons and arid/mountainous regions.