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The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast. [ABSTRACT FROM AUTHOR]

• Vegetation growth contributes 85.4–90.5% of the increased water consumption. • 94.3–98.8% of global vegetation land are sustainable due to rising precipitation. • Unsustainable regions will continuously expand due to the greening world. The global vegetation cover is increasing and is expected continue increasing till 2100, owing to CO 2 fertilisation and afforestation. This greening phenomenon may create conflicting demands for water between ecosystems, atmosphere, and humans, and has aroused a series of controversies. For exploring the effects of greening phenomenon on terrestrial water availability, we estimated the balance between water supply (precipitation and snowmelt runoff) and water consumption (evaporation, human water use, transpiration, and interception losses) in the global vegetation land based on the outputs of 30 models from the Coupled Model Intercomparison Project Phase 6. Water consumption showed an increasing trend in most regions of global vegetation land for the period 1982–2016 as well as 2040–2100, mainly driven by the increased transpiration and interception losses for vegetation growth (contributing 85.4–90.5%). Although the greening phenomenon widely exacerbates water shortages, 94.3–98.8% of the global vegetation land was still found to be sustainable during 1982–2100, due to the water supply exceeding the total water consumption. More attention should be paid to the unsustainable regions where the total water consumption exceeded the total water supply. Such areas were continuously found to increase from 2.1–5.3% (1982–2016) to 5.4–5.7% (2040–2100) of the global vegetation land. We predict that Oceania, Northern India, the southern coastal regions of North America will be transformed from sustainable regions in 1982–2016 to unsustainable regions in 2040–2100. [ABSTRACT FROM AUTHOR]

Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998–2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR. [ABSTRACT FROM AUTHOR]