Atmospheric Relief vs. Hydrological Buffering: How Changing Precipitation Characteristics Regulate Gross Primary Productivity in Central Asian Drylands

Global-warming-induced shifts in precipitation regimes profoundly affect terrestrial vegetation productivity; however, the pathways through which precipitation characteristics regulate gross primary productivity (GPP) remain insufficiently understood. Using meteorological records, satellite remote-sensing products, and reanalysis data from 2000 to 2024, we quantified GPP responses to changing precipitation regimes in Central Asian drylands through superposed epoch analysis, random forest and TreeSHAP analyses, and cross-lagged structural equation modeling (CB-SEM). Against a background of increasing annual precipitation (1.79 mm yr−1, p = 0.014), synchronous increases in precipitation frequency and intensity enhanced dryland productivity, with increasing GPP (0.28 g C m−2 yr−1, p = 0.726). The two precipitation-frequency metrics increased across 75.10–82.46% of the study area, whereas the intensity metrics increased across 64.16–81.67%. Frequent precipitation rapidly reduced vapor pressure deficit (VPD) and maintained shallow soil moisture, producing GPP anomaly gains ~0.5 g C m−2 8 d−1 across extensive Central Asian plains. In contrast, high-intensity precipitation replenished deep soil moisture and sustained GPP anomaly gains of 0.5–1.5 g C m−2 8 d−1 in the semi-arid steppe of northern Kazakhstan. CB-SEM identified VPD as the strongest direct climatic driver of GPP (−0.482), followed by soil moisture (0.290) and temperature (0.283). Precipitation frequency directly reduced VPD (−0.016), whereas precipitation intensity positively affected soil moisture (0.057). The frequency-mediated atmospheric pathway was strongest during the initial t (0–8-day) response period, while the intensity-mediated soil-moisture pathway became increasingly important after 8 days. These findings reveal complementary atmospheric-regulation and soil-moisture-buffering mechanisms and provide a process-based framework for improving the representation of dryland carbon–water coupling under changing precipitation regimes.