The relations between monthly-seasonal soil moisture and precipitation variability are investigated by identifying the coupled patterns of the two hydrological fields using singular value decomposition (SVD). SVD is a technique of principal component analysis similar to empirical orthogonal functions (EOF). However, it is applied to two variables simultaneously and is capable of extracting spatial patterns of one variable which are closely connected to variability of the other. Simulation is performed with a regional climate model to reproduce soil moisture and precipitation in east Asia. It is found that, of a number of leading soil moisture SVD patterns, those representing meridional anomalies have closer relationships to subsequent precipitation variability. The corresponding atmospheric variability is characterized by opposite anomalies between the middle and low troposphere and by comparable anomalies between the middle and low latitudes. These patterns occur more frequently during spring and summer. The time lag correlations of the SVD expansion series with soil moisture leading precipitation are much greater than those of the original data series and EOF expansion series, suggesting that predictability of monthly-seasonal precipitation variability could be improved by using soil moisture in the form of its coupled SVD patterns with precipitation.