Precipitation is an important control of soil moisture on land. Thus, many studies have focused on understanding the influences of mean or total precipitation variability on soil moisture. However, the relationship between precipitation intermittency (the temporal distribution of rainfall events) and soil moisture variability remains largely underexplored. This question requires more attention as climate models are known to be deficient in their representation of precipitation intermittency (PI), and PI is projected to increase in a future warmer climate, potentially affecting soil moisture variability.

In this study, we examine the associations between seasonal PI and soil moisture in observation-based datasets (ERA5, MSWEP, and GLEAM) and model simulations (CESM2 Large Ensembles – LENS2) for the period 1981–2020. To quantify the associations between PI and soil moisture, we perform a conditional regression analysis of 10cm soil moisture onto a metric of PI (number of wet days in a season), after removal of the influence of total seasonal precipitation from each variable.

The result suggests that in many regions, higher PI leads to reduced soil moisture under the same amount of seasonal precipitation. These associations are explained by increased runoff under higher PI. The seasonal spatial patterns of the magnitude and sign of the linkage between PI and runoff align with the seasonal patterns of precipitation-runoff and precipitation-evapotranspiration interactions. Spatial consistency in the associations is found between the observations and CESM2, although noticeable differences exist in the magnitudes of the regression coefficients between these two datasets. In general, the associations are weaker in CESM2. Quantifying these associations is crucial in understanding the influence of PI on soil moisture and potential future changes in this influence. In the next step, we will focus on examining how these associations may change by the end of the century.