Abstract The simultaneous occurrence of droughts and floods in neighboring regions amplifies the threats posed by droughts and floods individually. Nonetheless, few studies have been conducted to investigate the simultaneous occurrence of drought and flood events. Here we explore the spatiotemporal characteristics and the shift pattern of droughts and pluvials over Eastern China from a three‐dimensional perspective, using the self‐calibrated Palmer Drought Severity Index and the Climate Research Unit data set as well as four regional climate model simulations. We find that Eastern China experienced droughts and pluvials simultaneously in different locations during boreal summer, and it is projected to simultaneously experience more frequent and more intense droughts and pluvials under a warming climate. Specifically, we investigate the pattern of more pluvials in Southeast China and more droughts in Northeast China for the historical period of 1975–2004. This pattern dynamically evolves under climate warming: the pluvial‐dominated regime shifts from Southeast to Northeast China, while the drought‐dominated regime shifts from Northeast to Southeast China. The weakening strength of the western Pacific subtropical high and a northward displacement of the monsoon rain belt may both contribute to the pattern of more pluvials in Northeast China and more droughts in Southeast China. These findings provide insights into the development of adaptation strategies and emergency response plans for enhancing society's resilience to the spatial co‐occurrence of dry and wet extremes.

Physically based hydrologic models have been extensively used for hydroclimatic projections, but key challenges remain owing to the heavy computational burden and structural variability of physically based models. In this study, we develop a vine copula‐based polynomial chaos framework for improving multi‐model projections of hydroclimatic regimes at a convection‐permitting scale over the Dongjiang River Basin located in South China. Specifically, a deep neural network (DNN)‐based polynomial chaos expansion (PCE) is developed to significantly improve the efficiency of probabilistic hydrologic predictions. A vine copula multi‐model ensemble approach is also proposed to robustly combine hydrologic predictions generated from multiple DNN‐based PCEs to improve reliability and accuracy. To assess regional hydrologic responses to changing climate, multi‐decadal nested‐grid climate projections over the Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA) are developed using the convection‐permitting Weather Research and Forecasting (WRF) model with 4‐km horizontal grid spacing. Our findings reveal that the DNN‐based PCEs achieve comparable performance to the physically based hydrologic predictions with an extremely low computational cost. The vine copula multi‐model ensemble approach outperforms the Bayesian model averaging (BMA) by generating more accurate and reliable hydrologic predictions. The developed framework and physical models also lead to consistent projections of future changes in streamflow regimes. Our findings reveal that the projected increases in the frequency and intensity of extreme precipitation can lead to substantial increases in flood magnitudes, but the increases may not be obvious for river basins affected by multiple reservoirs.

Streamflow prediction plays a crucial role in water resources systems planning and the mitigation of hydrological extremes such as floods and droughts. Since a variety of uncertainties exist in streamflow prediction, it is necessary to enhance our efforts to robustly address uncertainties and their interactions for improving the reliability of streamflow prediction. This paper presents a stochastic hydrological modeling system (SHMS) for improving daily streamflow prediction by explicitly addressing uncertainties in error and model parameters as well as in forcing data and model outputs. Specifically, the SHMS merges the strengths of the ensemble Kalman filter and the particle filter algorithms for improving the effectiveness and robustness of daily streamflow assimilation. Factorial analysis of variance and variance-based global sensitivity analysis are performed to reveal parameter interactions affecting predictive performance and temporal dynamics of parameter sensitivities, maximizing the accuracy of streamflow prediction. The SHMS has been applied to the Guadalupe River basin located in Texas of the United States to demonstrate feasibility and applicability. Our findings indicate that the SHMS improves upon the well-known ensemble Kalman filter for sequential estimation of hydrological model parameters through a more rapid and accurate convergence of model parameters in streamflow simulation. The SHMS also demonstrates a higher level of skill in streamflow prediction compared to the conditional vine copula model. The proposed SHMS can be applied straightforwardly to other river basins for probabilistic hydrological prediction.

Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. It is of great significance to understand the past and future changes of rainfall erosivity and its implications in different regions of China on a national scale to promote soil and water conservation planning. In this paper, China is divided into eight first-class areas of soil and water conservation zoning. The multi-climate model and multi-emission scenario approach to characterize the spatiotemporal projection of rainfall erosivity and assess variations of rainfall erosivity in China consists of 5 regional climate models (RCMs) and two Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) scenarios for the baseline period (1986-2005) and future periods (2071-2090). The result indicates that compared with observational data, the performance of the MCLM and PRECIS is better than other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. There is an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs, for all the RCMs, with an average change in erosivity of about 13.0% and 22.1% under RCP4.5 and RCP8.5 respectively. Under the RCP 8.5 scenario, the absolute value of rainfall erosivity increased by 797.8 and 969.7 MJ·mm·hm ⁻² ·h ⁻¹ respectively in the South China red soil region and the North China mountainous region, indicating that climate warming will greatly enhance the potential erosion capacity of rainfall in these two areas. Meanwhile, the Southwest China karst region and Qinghai Tibet Plateau region are more sensitive to radiation forcing. According to the changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions, taking soil and water conservation measures is conducive to reducing the risk of soil erosion caused by climate change.

Plain Language Summary Heat waves and heavy rainfall have profound impacts on humans, ecosystems and society. Despite the well‐understood mechanisms of heat waves and heavy rainfall, current knowledge on the abrupt transitions from deadly heat waves to devastating downpours remains unclear as they are usually treated as isolated events in previous studies. In this study, we investigate the occurrence of heat waves followed by heavy rainfall in China by revealing the probability of occurrence and underlying mechanisms as well as future changes of compound extremes. We find that approximately for every four heat wave events, one of which was followed by heavy rainfall within 7 days during 1981–2005, which is much higher than that expected by chance. Furthermore, we highlight that the shorter and hotter heat waves are more likely to be followed by heavy rainfall compared with other heat waves. Such consecutive heat wave and heavy rainfall events are projected to occur more frequently in China under a warming climate. Our study offers meaningful implications for policymakers and stakeholders to better implement adaptation and mitigation solutions that can help reduce the negative consequences of this type of back‐to‐back extremes (consecutive heat wave and heavy rainfall events).