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In recent decades, India has witnessed an increase in the intensity, frequency, and spread of extreme weather events. The widespread increase in extreme precipitation over the Western Coast of India is a matter of great concern. The factors contributing to such devastating extreme precipitation remain unclear due to the variability present in meteorological and oceanic variables and associated large‐scale circulations. Using reanalysis and observed datasets, we attempted to identify a combination of dynamic, thermodynamic, and cloud microphysics processes contributing to the anomalous precipitation from August 1 to 10, 2019 against its climatology. Our key findings highlight the crucial role of warm sea surface temperatures (anomaly >1.4°C), outgoing longwave radiation (anomaly <−50 W·m−2), and atmospheric temperature (anomaly over the ocean is >1.6°C) in enhancing the moisture‐holding capacity of the atmosphere by almost 10%. This elevated moisture, propelled by intensified low‐level winds (anomalies exceeding 4 m·s−1), leads to a shift from ocean to land. Notably, we observe that vertical updrafts (anomalies >−0.4 m·s−1) contribute to increased atmospheric instability and moisture convergence. The presence of an ample amount of cloud hydrometeors, with anomalies surpassing 2.5 × 10−4 kg·kg−1, establishes conditions conducive to sustained intense precipitation. Our findings deepen our understanding of the complex relationships between ocean and atmospheric dynamics, and wind patterns, and emphasize their pivotal influence on regional weather patterns and land surface hydrology. [ABSTRACT FROM AUTHOR]

Precise evapotranspiration (ET) estimation is critical for agricultural water management, particularly in water-stressed developing countries. Vapor Pressure Deficit is one of the ET parameters that has a significant impact on its calculation (VPD). This paper forecasts VPD using ensemble learning-based modeling in eight different regions (Dakahliyah, Gharbiyah, Kafr Elsheikh, Dumyat, Port Said, Ismailia, Sharqiyah, and Qalubiyah) in Egypt. In this study, six machine learning algorithms were used: Linear Regression (LR), Additive regression trees (ART), Random SubSpace (RSS), Random Forest (RF), Reduced Error Pruning Tree (REPTree), and Quinlan's M5 algorithm (M5P). Monthly vapor pressure data were obtained from the Japanese 55-year Reanalysis JRA-55 from 1958 to 2021. The dateset has been divided into two segments: the training stage (1958–2005) and the testing stage (2006–2021). Five statistical measures were used to evaluate the model performances: Correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative absolute error (RAE), and Root Relative Squared Error (RRSE), across both training and testing stages. RF model outperformed the rest of the models [CC = 0.9694; MAE = 0.0967; RMSE = 0.1252; RAE (%) = 21.7297 and RRSE (%) = 24.0356], followed closely by REPTree and RSS models. On the other hand, M5P model performance remained moderate and both LR and AR model were the worst. During the testing stage, RF outperformed the rest of the models in terms of (which statistic), followed closely by REPTree and RSS models. On the other hand, M5P performance remained moderate and both LR and AR models were the worst. This study recommended using the RF model for future hydro-climatological studies in general, and vapor pressure deficit modeling and prediction in particular. This study enables future magnitudes to be predicted, alerting the authorities and administrators involved to focus their policy-making on more specific pathways toward climate adaptation. • Six machine learning models (LR, BART, RSS, RF, REPTree, and M5P) were developed for modeling VPD. • Long time series of eight Egyptian regions was used for estimation of VPD. • The RF model outperformed the rest models. • The performance of M5P and LR models was the second rank, while RSS model performance remained moderate. [ABSTRACT FROM AUTHOR]