AUTHOR=Chai Hua , Ma Jianying , Zhang Jinwei , Li Junqin , Meng Bo , Wang Chengliang , Pan Duofeng , Li Jie , Sun Wei , Zhou Xuhui TITLE=Nonlinear responses of ecosystem carbon fluxes to precipitation change in a semiarid grassland JOURNAL=Frontiers in Plant Science VOLUME=Volume 16 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1519879 DOI=10.3389/fpls.2025.1519879 ISSN=1664-462X ABSTRACT=Carbon (C) fluxes in semiarid grasslands subject to precipitation variability play a critical role in the terrestrial C cycle. However, how ecosystem C fluxes respond to variability in precipitation (both decreases and increases precipitation along a gradient) remains unclear. In this study, we conducted a three-year field experiment in a semiarid grassland, with six precipitation treatments (precipitation decreased by 70%, 50%, and 30% [P–70%, P–50%, and P–30%], natural precipitation [P+0%], and precipitation increased by 30% and 50% [P+30% and P+50%]) to examine how variations in precipitation influence ecosystem C fluxes, specifically focusing on gross ecosystem productivity (GEP), ecosystem respiration (ER), and net ecosystem CO2 exchange (NEE). We found that both decreased and increased precipitation significantly altered the GEP (from –26% to 14%), but only decreased precipitation significantly reduced the ER and NEE (from 1% to 31%), relative to their values during natural precipitation. This suggests that ecosystem C fluxes are more sensitive to decreased precipitation, and respond nonlinearly to the precipitation gradient. Furthermore, structural equation modeling indicated that the soil water content was the primary controlling factor driving changes in ecosystem C fluxes. Our research underscores the nonlinear response of ecosystem C fluxes to changes in precipitation within semiarid ecosystems, particularly their sensitivity to extreme drought. Considering this nonlinear response, it is crucial to improve dynamic models of the C cycle and predict ecosystem responses to precipitation variability.