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Microbes inhabiting deep soil layers are known to be different from their counterpart in topsoil yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (>1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18‐m depth profiles at 20–50‐cm intervals across contrasting aridity conditions in semi‐arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant‐derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa–taxa and bacteria–fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep‐soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria–fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that, even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole‐soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios. [ABSTRACT FROM AUTHOR]

• Noninvasive approach for obtaining soil water content is proposed. • Soil water content is indirectly obtained from electrical resistivity. • Prediction models for soil water content were built for different soil conditions. • The proposed approach was applied at the Chinese Loess Plateau for validation. • Models for deep soil gave better results and may be applied to other loess regions. Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT - derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. [ABSTRACT FROM AUTHOR]