# Diverse Roles of Previous Years' Water Conditions in Gross Primary Productivity in China
## 1 Introduction
Vegetation growth may not primarily be driven by present water conditions, but earlier water conditions may have the most impact on vegetation growth [38]. There are significant differences in the effects of annual and interannual changes in water deficits on vegetative productivity. Because vegetative productivity and water deficits fluctuate greatly and are complicated by seasonal and monthly differences, it is more typical to analyse the effects of interannual changes in water deficits. Vegetation tends to be more sensitive to long-term drought than to short-term drought [39]. Short-term drought can be understood as a drought in which the water conditions of the previous years are normal, and the water conditions of the current year are in a drought; long-term drought can be understood as a drought in which the water conditions of the previous years and the current year are in a drought. These produce significant differences in the effects of water conditions on vegetative growth [40; 41]. [PERSON] et al. (2013) [42] and [PERSON] et al. (2020) [43] found that there was a legacy effect in the transition from dry years to wet years (or vice versa). There was a lower aboveground net primary production (ANPP) than predicted if the precipitation of the previous year was lower than the precipitation of the current year (or vice versa), suggesting that the combination of dry and wet in the previous year and current year is significant to the analysis of the time-lag effect of the previous years' water deficit. Correlation analysis is used in many studies to determine the optimal time scale using the maximum correlation coefficient between the vegetation index (e.g., normalized difference vegetation index (NDVI), ring-width indices (RWIs) and aboveground net primary production (ANPP)) and the drought index (e.g., standardized precipitation evapotranspiration indices (SPEIs)) at present. In some regions, the time scale of vegetative response to drought is more than 24 months [13; 44], whereas other studies that used the same method found that the cumulative lag in vegetative response to drought was five months [45] or even less [38]. Compared with correlation analysis, natural experiments can analyse selectively in detail according to the purpose of the study and can better reflect the response of vegetative growth to drought [30]. Given that GPP is highly uncertain under the influence of drought that may happen in a different time and understanding the role of the previous year's drought is necessary to accurately predict GPP, we aimed to accomplish the following three objectives in this study: (1) identify the water-limited regions where the potential impact of drought should be emphasized in China, (2) apply a machine learning algorithm (i.e., random forest) to evaluate the importance of water conditions at different times on GPP, from which we could quantify the relative importance of the water condition of previous years, and (3) design a series of water deficit scenarios and, accordingly, build a series of regression models to reveal the role of the previous year's water conditions on GPP.