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In the year 2019, the middle and lower reaches of the Yangtze River (MLRYR) experienced an unprecedented summer‐autumn drought (SAD) driven by dry‐hot conditions [high near‐surface air temperatures (T) and low precipitation (P)], causing substantial agricultural and economic losses. However, the influence of anthropogenic climate change (ACC) on these dry‐hot conditions and their impacts on SAD occurrences remains uncertain. Here, both observations and simulations show that an ACC‐driven T increase led to the greater likelihood of dry‐hot conditions from August to November 1901–2020 in MLRYR. Using the self‐calibrating Palmer drought index (scPDSI) to assess SAD severity, we find an increasing likelihood of SAD occurrence (from 33.3% in 1901–2000 to 85.7% in 2001–2020) in MLRYR associated with more frequent dry‐hot conditions. Under a business‐as‐usual scenario, future dry‐hot association is projected to be stronger, with exceptional dry‐hot conditions to increase by +10% per century. ACC‐induced increase in dry‐hot conditions would elevate the likelihood of SAD events like the 2019 event from 1.59% (1961–2020) to 17.82% (2041–2100). Therefore, effective measures are needed in MLRYR to adapt to increasing dry‐hot conditions and associated SAD occurrences under anthropogenic warming. Plain Language Summary: The 2019 summer‐autumn drought (SAD) in the middle and lower reaches of the Yangtze River (MLRYR) caused a substantial loss of agriculture and economy. This SAD was accompanied by dry‐hot conditions, that is, high near‐surface temperature (T) and low precipitation (P). The impact of anthropogenic climate change (ACC) on these dry‐hot conditions and the past and future responses of SADs to them is unclear. To understand this, the present study investigates the historical and projected likelihood of dry‐hot conditions and droughts like those of the 2019 SAD under ACC in MLRYR. We found that ACC‐induce increases in T raises the likelihood of dry‐hot conditions from August–November 1901–2020 in MLRYR, as observed in both observations and simulations. The likelihood of SAD occurrence increased from 33.3% in 1901–2000 to 85.7% in 2001–2020 in MLRYR. Under the high‐emission scenario, the frequency of exceptional dry‐hot conditions is projected to increase by +10% per century. ACC‐increased dry‐hot conditions are expected to substantially elevate the likelihood of such SAD events as the 2019 event from 1.59% (1961–2020) to 17.82% (2041–2100). These findings help us understand how ACC influences the past and future likelihood of dry‐hot conditions and SAD occurrences in MLRYR. Key Points: ACC has increased the likelihood of dry‐hot conditions during August–November 1901–2020, leading to more SADs in MLRYRUnder a business‐as‐usual scenario, dry‐hot is projected to strengthen, with exceptional dry‐hot conditions grown by +10% per centuryUnder ACC‐increased dry‐hot conditions, the likelihood of SADs like the 2019 event increased from 1.59% (1961–2020) to 17.82% (2041–2100) [ABSTRACT FROM AUTHOR]

In this study, we develop a spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method to improve probabilistic sub-seasonal precipitation forecast skill over 17 hydroclimatic regions in China. The calibration model is established by post-processing ECMWF raw forecasts using the Bayesian joint probability (BJP) approach. The bridging models are built using large-scale atmospheric intraseasonal predictors, including zonal wind at 200 hPa (U200) and 850 hPa (U850); an outgoing longwave radiation anomaly (OLRA); and geopotential height at 200 hPa (H200), 500 hPa (H500), and 850 hPa (H850) defined by the STP method. The calibration model and the bridging models are then merged through the Bayesian modelling averaging (BMA) method. Our results indicate that the forecast skill of the calibration model is higher compared to bridging models when the lead time is within 5–10 d. The U200- and OLRA-based bridging models outperform the calibration model in certain months and certain regions. The BMA-merged forecasts take advantage of both calibration models and bridging models. Meanwhile, the BMA-merged forecasts also show high reliability at longer lead times. However, some improvements to reliability are still needed at shorter lead times. These findings demonstrate the great potential to combine dynamical models and statistical models in improving sub-seasonal precipitation forecasts. [ABSTRACT FROM AUTHOR]

High computational cost is often the most limiting factor when running high‐resolution hydrodynamic models to simulate spatial‐temporal flood inundation behavior. To address this issue, a recent study introduced the hybrid Low‐fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behavior of flood inundation extent by upskilling simulations from a low‐resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high‐resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high‐resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent‐based LSG model rather than deriving it from water depth predictions. Plain Language Summary: Every year, lives are lost, and infrastructure is destroyed due to floods. This highlights the need for fast and accurate flood predictions to inform flood forecasting and risk assessments. However, predicting flood inundation in high resolution is often not practically feasible due to the high computational cost involved in running complex computer models. Simplified computer models can be used to provide faster flood predictions, but they lack the accuracy provided by complex models. To address this issue, this study evaluates an alternative method based on the combination of a fast simple model together with an advanced spatial feature matching method. The advanced spatial feature matching method is used to convert the predictions obtained from the simple model to accurate predictions of flood inundation depth and extent. The new approach is applied to a large floodplain in Australia and different adaptations are explored to optimize the procedure and ensure robust performance. The new approach is compared to the use of a traditional complex model and a previous approach that only predicted inundation extent. The new approach shows similar accuracy to the traditional complex model while being 12 times faster, thereby making it more practically useful for flood risk assessments. Key Points: A flood inundation model is developed, based on Low‐fidelity hydrodynamic modeling, Spatial analysis, and Gaussian Process learning (LSG)The LSG model predicts water depths with a mean Root Mean Square Error (RMSE) of 4 cm and a standard deviation of 5 cmThe LSG model is 12 times faster than a traditional high‐resolution hydrodynamic model [ABSTRACT FROM AUTHOR]