As the largest component of carbon export from terrestrial ecosystems, ecosystem respiration (RECO), together with vegetation productivity, determines the carbon stock changes in terrestrial ecosystems, so it is crucial to reveal the response of RECO to climate change. However, the simulation of RECO is usually inaccurate due to the neglect of the lagged response of respiration to changes in water conditions. In this study, we integrated meteorological data, remote sensing data, and soil data, and introduced the indicators of the previous water conditions into the deep learning model to simulate RECO. The conclusions are as follows: (a) There is a 1–2 years' lagged response of RECO to changes in water conditions. (b) It is necessary to consider the influence of previous water conditions when simulate RECO. It not only improved the simulation accuracy of RECO, but also reflected its inter‐annual fluctuations and change trends more accurately, avoiding the underestimation of RECO inter‐annual fluctuations. (c) There is an inconsistency in carbon input and carbon output change trends which impacts the carbon sink pattern and potential of terrestrial ecosystems. The growth trend of Gross Primary Production (GPP) in global terrestrial ecosystems is greater than that of RECO, with a trend of increasing carbon sinks, especially in the northern extra‐tropics; while the carbon sink capacity of tropical regions has gradually saturated, showing that the change trend of RECO is close to that of GPP, which poses a potential risk to the sustainable carbon sink capacity of global ecosystems in the future.

Gross primary productivity is one of the most important indicators of ecosystem function, which is related to water conditions and shown high interannual variation. Due to the time-lag effect, not only the current water condition but also the previous water conditions (e.g., one year before) impact the gross primary productivity (GPP). Revealing the impacts of current and previous years’ water status is currently a hot topic. In this study, we designed a series of water deficit scenarios based on the meteorological dataset of the Climatic Research Unit (CRU) and then analysed the responses of the remote sensing-based moderate resolution imaging spectroradiometer (MODIS) gross primary productivity (GPP) in China, from which the role of water deficit in time periods was evaluated. The results indicate that the impact of climate factors (i.e., water, temperature and radiation) on GPP has a high spatial heterogeneity and that water-limited regions that are primarily distributed in North and Northwestern China show a stronger water-GPP relationship than water-unlimited regions. The water deficit that occurred in different periods had a variable impact on GPP. Specifically, GPP was primarily controlled by the current year’s water conditions in the water-limited regions, with the importance value of 52.8% (the percentage of Increased Mean Square Error, %IncMSE) and 3.8 (the mean decrease in node impurity, IncNodePurity), but at the same time, it was conditionally affected by the water status in the previous year, with the importance value of 20.4% (%IncMSE) and 0.6 (IncNodePurity). The role of water in previous years is multifarious, which depends on the water conditions of the current year. The results revealed by the scenarios indicate that the influence of water conditions in the previous year was not statistically significant when the water conditions of the current year were in a drought. In contrast, when the current year’s water conditions were normal or wetter, the water conditions in the previous year (i.e., one-year time lag) were also important and the increase of GPP significantly depended on the water condition (p < 0.05). The diverse roles of water conditions in previous years on GPP and its non-ignorable time-lag effect revealed in this study imply that not only the current year’s water condition but also its dynamic changes in previous years should be considered when predicting changes in GPP caused by climate change.

Spatiotemporal patterns of water stress caused by global warming has significantly affected gross primary productivity (GPP). However, its impact is hard to capture as the water stress of different timescales simultaneously influence GPP through the effects of time lag and legacy. As a result, synthetically considering the roles of water stress in the current year and previous years in GPP is very important for accurate modeling of GPP. In this study, we introduced the water stress indicators of standardized precipitation evapotranspiration index (SPEI) at different timescales, GPP observations from global flux network and the remote sensing‐based indexes to models of convolutional neural network (CNN). We built the optimal CNN model which effectively simulated the spatial patterns of GPP and detected their trends in interannual variation. The findings were as follows: (1) The CNN model with introduction of multi‐timescale SPEIs that reflect both current and previous years' water conditions improved the accuracy of modeled GPP. This result indicated that although GPP is most sensitive to current water conditions, it is also clearly affected by previous years' water conditions. (2) The optimal CNN model that considered previous years' water conditions not only aptly simulated the spatial distribution of GPP, but also showed advantages in simulating the interannual fluctuations of GPP and its response to drought. Ignoring the previous water conditions will underestimate the interannual fluctuations of GPP and the impact of drought on GPP.

Water availability is one of the most important resources for forest growth. However, due to its complex spatio-temporal relationship with other climatic factors (e.g., temperature and solar radiation), it paradoxically shows both positive and negative correlations (i.e., dual roles) with forest vigor for unknown reasons. In this study, a multiperspective analysis that combined the deficit of the Normalized Difference Vegetation Index (dNDVI) and multitimescale Standardized Precipitation Evapotranspiration Index (SPEI) was conducted for the forests in China, from which their correlation strengths and directions (positive or negative) were linked with spatio-temporal patterns of environmental temperature (T) and water balance (WB) (i.e., precipitation minus potential evapotranspiration). In this way, the reasons for the inconsistent roles of water were revealed. The results showed that the roles of water availability greatly depended on T, WB, and seasonality (i.e., growing or pregrowing season) for both planted and natural forests. Specifically, a negative role of water availability mainly occurred in regions of T below its specific threshold (i.e., T ≤ T_threshold) during the pregrowing season. In contrast, a positive role was mainly observed in warm environments (T > T_threshold) during the pregrowing season and in dry environments where WB was below its specific threshold (i.e., WB ≤ WB_threshold) during the growing season. The values of T_threshold and WB_threshold were related to the vegetation type, with T_threshold ranging from 1.3 to 4.7 °C and WB_threshold ranging from 129.1 to 238.8 mm/month, respectively. Our study revealed that the values of T_threshold and WB_threshold for a specific forest were stable, and did not change with the SPEI time-scales. Our results reveal the dual roles of water availability in forest vigor and highlight the importance of environmental climate and seasonality, which jointly affect the roles of water availability in forest vigor. These should be considered when monitoring and/or predicting the impacts of drought on forests in the future.