Difference between NCEP's 20th Century daily Forecast and Analysis variables
Some variables in reanalyses have to be forecast and cannot be assimilated to allow the model to converse mass, momentum and energy. Precipitation is a good example. The model will assimilate observations which give information about moisture (someone who knows more about assimilation may want to chirp in here). This will be used in the analysis (e.g. 00z) and is forecast forward. Daily precipitation is therefore given as the accumulation of precipitation from 00z analysis -> 12z forecast and the 12z analysis -> 24z forecast.

There is a hierarchy of precipitation datasets each with pros and cons:


Gauge data is often considered the best but it very patchy (not to mentions instrument issues). You can use kriging to make spatial maps. This paper may be of interest.
Satellite datasets such as GPCP and TRMM give better spatial information but subject to issues over land, for example (I think). The are also subject to trackiness (only get data underneath/near the satellite).
Reanalyses can provide spatial and temporal homogeneity but it is a model and 'all models are wrong, but some are useful'.  
Datasets are emerging combining both gauge, satellite and reanalyses such as in this paper.