Evapotranspiration (ET) is usually difficult to estimate at the regional scale due to scarce direct measurements. This study uses the water balance equation to calculate the regional ET with observations of precipitation, runoff, and terrestrial water storage changes (TWSC) in nine exorheic catchments of China. We compared the regional ET estimates from a water balance perspective with and without considering TWSC (ET_WB: ET estimates with considering TWSC, and ET_PQ: ET estimates from precipitation minus runoff without considering TWSC). Results show that the regional annual ET ranges from 417.7 mm/yr to 831.5 mm/yr in the nine exorheic catchments based on the water balance equation. The impact of ignoring TWSC on calculating ET is notable, as the root mean square errors (RMSEs) of annual ET between ET_WB and ET_PQ range from 12.0–105.8 mm/yr (2.6–12.7% in corresponding annual ET) among the exorheic catchments. We also compared the estimated regional ET with other ET products. Different precipitation products are assessed to explain the inconsistency between different ET products and regional ET from a water balance perspective. The RMSEs between ET estimates from Gravity Recovery and Climate Experiment (GRACE) and ET from land surface models can be reduced if the deviation of precipitation forcing data is considered. ET estimates from Global Land Evaporation Amsterdam Model (GLEAM) can be improved by reducing the uncertainty of precipitation forcing data in three semiarid catchments. This study emphasizes the importance of considering TWSC when calculating the regional ET using a water balance equation and provides more accurate ET estimates to help improve modeled ET results.

Irrigation supports agricultural production, but widespread use of irrigation can perturb the regional and global water cycle. The one‐way coupled irrigation scheme used in some land surface models and Earth system models assumes that surface water demand is always met and ignores the surface water constraints, leading to overestimation of surface water usage, underestimation of groundwater pumping, and unrealistic simulation of their seasonal variability. To better represent the irrigation processes, a two‐way coupled irrigation scheme is developed within the Energy Exascale Earth System Model (E3SM). The new irrigation scheme simulates irrigation water demand and applies irrigation water in E3SM Land Model (ELM), which is coupled to a river routing model and a water management model (MOSART‐WM) that simulate streamflow, reservoir operations, and irrigation water supply. With two‐way coupling, surface water irrigation is constrained by the available runoff, streamflow, and reservoir storage. Simulations were performed for 1979–2008 at 0.5° spatial resolution to estimate irrigation surface water and groundwater use and their seasonality in global and large river basin scales. Compared to one‐way coupling, the two‐way coupling scheme (1) estimates less surface water withdrawal and less return flow due to the surface water constraint; (2) better represents groundwater recharge and groundwater level decline at global scale; and (3) is able to capture the seasonal dynamics of irrigation water allocations which reflect the local water conditions. The new development is an important step to more realistically account for the interactions between human water use and the terrestrial water cycle in an Earth system model.

Evapotranspiration (ET) combines the land‐atmosphere water, energy, and carbon cycles and plays a critical role in climate studies. However, ET is difficult to quantify accurately. This study compares three 1982–2013 ET data sets for China based on surface water balance values, a modified Penman‐Monteith (MPM) model and reanalysis data. Water balance ET data, which have been widely used as benchmark data for regional ET, were calculated with and without considering reservoir water storage. MPM ET values were calculated from combinations of input data from different sources, including solar radiation and meteorological data. The MPM model is primarily energy determined, although the impacts of vegetation, soil moisture, air temperature, and wind speed on ET are also considered. The reanalysis ET data used in this study are derived from atmospheric and off‐line land reanalyses. We examined their spatial patterns, interannual variabilities, trends, and key drivers. We found that MPM ET is consistent with water balance ET in detecting ET trends for 1997 to 2013, whereas reanalysis ET is not. The trends and interannual variabilities of ET found via reanalyses are primarily determined by precipitation patterns. However, the precipitation data derived from the atmospheric reanalyses present significant uncertainties. Off‐line land reanalyses correct these biases but do not consider terrestrial water storage changes resulting from reservoir construction and interannual variability in vegetation. Both omissions may produce unreliable trends of ET in reanalyses for China. This study will help improve current ET simulations and contribute to further extending climate studies.

Despite the serious threats posed by floods, the driving mechanisms of floods are still not well understood. Here we apply a physically based inundation model coupled with a river routing model (Model for Scale Adaptive River Transport (MOSART)) within the Energy Exascale Earth System Model (E3SM) framework to investigate flood inundation dynamics. After calibration using observed streamflow and satellite‐derived flood extent, the model is used to simulate global flood inundation from 1953 to 2004. The mean date and seasonality of annual maximum flood, defined based on flood extent, exhibit significant regional differences across 16 major basins. Generally, soil moisture and monthly maximum daily rainfall (MMR) are the dominant drivers of flood in tropical basins while monthly maximum daily snowmelt (MMS) is the dominant driver in high‐latitude basins. From 1953–1982 to 1975–2004, significant changes in flood generation mechanisms are found in some basins such as Amazon, Lena, Yenisey, and Kolyma. Analysis of the rainfall seasonality and water balance at grid scale reveals a stronger rainfall seasonality in the Amazon during the later period that increases the synchrony between extreme rainfall and wet soil. With high antecedent soil moisture coinciding with rainfall, MMR contributes more to flood in the later period. Fewer extreme rainfall events and increasing soil moisture reduced the contribution of MMR and increased the role of MMS in floods in the Lena and Yenisey basins, respectively. Lastly, increased soil moisture and frequency of large MMS reduced the contribution of the latter to floods in the Kolyma basin.

The spatiotemporal characteristics of extreme precipitation intensity are crucial for hydroclimatic studies. This study delineates the spatiotemporal distribution features of extreme precipitation intensity across China from 2001 to 2019 using the gridded daily precipitation dataset CN05.1, constructed from an observation network of over 2400 stations. Furthermore, we evaluate the reliability of 12 widely used precipitation datasets (including gauge-based, satellite retrieval, reanalysis, and fusion products) in monitoring extreme precipitation events. Our findings indicate the following: 1) CN05.1 reveals a consistent spatial distribution characterized by a decline in extreme precipitation intensity from the southeastern coastal regions toward the northwestern inland areas of China. From 2001 to 2019, more pronounced declining intensity trends are discernible in the northern and southwestern regions of China, whereas marked increasing trends manifest in the northeastern and the Yangtze River plain regions. National mean extreme precipitation indices consistently exhibit significant increasing trends throughout China. 2) Datasets based on station observations generally exhibit superior applicability concerning spatiotemporal distribution. 3) Multisource weighted precipitation fusion products effectively capture the temporal variability of extreme precipitation indices. 4) Satellite retrieval datasets exhibit notable performance disparities in representing various intensity indices. Most products tend to overestimate the increasing trends of national mean intensity indices. 5) Reanalysis datasets tend to overestimate extreme precipitation indices, and inadequately capture the trends. ERA5 and JRA-55 underestimate trends, while CFSR and MERRA-2 significantly overestimate the trends. These findings serve as a basis for selecting reliable precipitation datasets for extreme precipitation and hydrological simulation research in China. Significance Statement: Extreme precipitation events have increasingly become more widespread, posing significant threats to human lives and property. Accurately understanding the spatiotemporal patterns of these events is imperative for effective mitigation. Despite the proliferation of precipitation products, their capacity to faithfully represent extreme events remains inadequately validated. In this study, we utilize a gauge-based dataset derived from over 2400 gauge stations across China to investigate the spatiotemporal changes in extreme precipitation events from 2001 to 2019. Subsequently, we conduct a rigorous evaluation of 12 widely used precipitation datasets to assess their efficacy in depicting extreme events. The results of this research offer valuable insights into the strengths and weaknesses of various precipitation products in depicting extreme events.