Joint variability in annual precipitation amounts across the territory of Siberia and Kazakhstan was analyzed using multifactor stochastic modeling tools. It is shown that there are statistically significant correlations between these variables, which satisfy the temporal stability condition. Such correlations that reflect the regularities in the formation of the annual precipitation field have also both a positive and a negative character. Positive correlations are observed between closely located weather stations and reflect mainly the local homogeneity of the formation of the annual precipitation field. They are reliably identified by methods of both multi-factor and single-factor statistics. Negative correlations are observed between the significantly more remote weather stations. They are revealed according to the condition of the significance of the deviation from zero for the negative coefficients of the variables in multi-factor linear regression models. Paired negative, statistically significant correlations between annual precipitation amounts at weather stations occur extremely rarely. From an applied perspective, the presence of significant regular correlations between annual precipitation amounts can be used to construct models based on such regularities, for an approximate reconstruction of missing data in the past or in problems of a combined forecasting of the expected annual precipitation for the territory under consideration in the future.Keywords: precipitation variability, analysis of interrelations, multifactor modeling, reliability of estimates, standing waves.