1. Intensified inter-annual fluctuations in precipitation could profoundly impact terrestrial ecosystems. However, how changes previous-year precipitation influence current ecosystem functioning (e.g., resource use efficiency) in semi-arid regions remains unclear.

2. In this study, water use efficiency (WUE) and light use efficiency (LUE) were investigated in a multi-year precipitation gradient experiment with seven treatment levels: 20%, 40% and 60% decreases and 20%, 40% and 60% increases in the amount of natural rainfall plus ambient precipitation. Plots receiving 60% less precipitation were representative of extreme dry years whereas the other treatment levels fell within the normal year-to-year range in precipitation change. Measurements made in both the post-treatment period (2013-2015) and the treatment period (2010-2012) provided an opportunity to quantify the legacy effects of precipitation on resource use efficiency (RUE).

3. Sensitivities of LUE to previous-year precipitation were not changed among treatments in 2013. However, asymmetric responses of RUEs (i.e., WUE and LUE) to previous-year precipitation were found in 2014-2015. WUE₂₀₁₄, WUE₂₀₁₅, LUE₂₀₁₄, and LUE₂₀₁₅ responded more strongly to previous normal decreased than increased precipitation. Importantly, they were more sensitive to previous extreme dry year (represented by 60% precipitation reduction) than normal wet year (represented by 60% precipitation increment). Aboveground net primary productivity (ANPP) rather than resource absorption (R_uptake) drove these asymmetric responses of RUE, and biomass of grasses further explained the asymmetric responses of ANPP.

4. This study reveals the non-linear responses of RUE to previous-year precipitation and highlighted that the legacy effects of precipitation on RUE can be ascribed to the changes in vegetation composition. Our findings can facilitate the prediction of the legacy effects of precipitation variation on grassland ecosystem functions in the future.

We harvested the aboveground biomass by species in August from 2013-2015 by two 0.15 m² quadrats per plot. This total aboveground biomass was then categorized into two functional groups: grasses and forbs. ANPP was taken as the total aboveground biomass in August. Community cover was measured in August concurrent with biomass sampling.

We calculated the community transpiration using the difference between community evapotranspiration and evaporation. Community evapotranspiration was measured using a Li-6400XT Portable Photosynthesis System (Li-Cor Inc., Lincoln, NE, USA) attached to a static transparent chamber (0.5×0.5×0.5 m³) three times a month. Ecosystem evaporation was measured using a Li-8100 Portable Photosynthesis System (Li-Cor Inc., Lincoln, NE, USA). Three months before the measurements, we cleared up the aboveground biomass and inserted a PVC circle (50 cm in depths) to cut off the ambient roots into the circle , in order to get undamaged soil environment without vegetation in PVC circles. We also measured the volumetric soil moisture content (VWC, 0–50 cm) using a portable SWC device (Diviner 2000, Sentek Pty Ltd, Balmain, Australia) six to ten times a month from 2013–2015.

In resource use efficiency (RUE) models, absorbed resources (R_uptake) are those water and light resources consumed during the growing season. The accumulated vegetation transpiration (Tr) refers to the community-level water consumption. Tr was calculated as the product of observed community transpiration and the time between two sampling dates during the growing season. The accumulated absorbed photosynthetically active radiation (aPAR) refers to the community-level light absorption. Here, the photosynthetically active radiation (PAR) was recorded in real-time by an Li-190SB quantum sensor (LI-Cor Inc., Lincoln, NE, USA) in an eddy covariance system located near the precipitation experiment. We calculated the aPAR by the product of community cover and the remainders of one minus reflectance and transmittance (Han et al. 2016).

WUE and LUE were calculated as the ratios of ecosystem production to R_uptake (Binkley et al., 2004). Here, ANPP was used for ecosystem production, and the specific equations for WUE and LUE calculations were as following:

WUE = ANPPTr  (1)    

LUE = ANPPaPAR  (2)   

where Tr and aPAR were the absorbed resources in the WUE and LUE models, respectively. To obtain unit-less versions of WUE and LUE, all measurements were converted into the same unit (g m^-2 year^-1).

The sensitivity of variables (Y) to previous-year precipitation (P-PPT) was calculated as the derivative of response function as follows:

Y = a×(P-PPT)^b  (3)

Sensitivity of Y to P-PPT = a×b×(P-PPT)^(b-1)  (4)

where the sensitivity of Y to P-PPT are the derivative of response function in equation (3). “a” and “b” are constant in equation (3). These sensitivities range from -1 to +1, their magnitudes represent the strength of sensitivity of Y to P-PPT. Their directions (positive/negative) represent the changes in Y are identical/opposite to the changes in previous-year precipitation amount.

Legacy effect on RUE was calculated as the differences between the observed RUE and the expected RUE (Reichmann, Sala & Peters 2013), the equations were as following:

Legacy_RUE = RUE_observed - RUE_expected   (5)

Where RUE_observed refers to RUE values in each plots of altered precipitation in 2013-2015, RUE_expected refers to the averaged RUE values in control plots in 2013-2015. Legacy_RUE refers to the values of legacy effects on RUE.