Jianhong Wang

The Eastern inland of Syria has a Mediterranean climate in the north and a tropical desert climate in the south, which results in a dry south and wet north climate feature, especially in winter. The circulation dynamics analysis of 16 winter strong precipitation events shows that the key system is the dry and warm front cyclone. In most cases (81–100% of the 16 cases), the moisture content in the northern part of the cyclone is higher than that in the southern part (influenced by the Mediterranean climate zone). The humidity in the middle layer is higher than that near the surface (uplifting of the dry warm front), and the thickness of the wet layer and the vertical ascending layer obviously expands upward (as shown by the satellite cloud top reflection). These characteristics lead to the moisture thermodynamic instability in the eastern part of the cyclone (dry and warm air at low level and wet and cold air at upper level). The cyclone flow transports momentum to the local humid layer of the Mediterranean climate belt and then causes unstable conditions and strong rainfall. Considering the limitations of the Syrian ground station network, the NCEP/CFSR global reanalysis data and MODIS aqua-3 cloud parameter data are used to build a multi-source factor index of winter precipitation from 2002 to 2016. A decision tree prediction model is then established and the factors index is constructed into tree shapes by the nodes and branches through calculating rules of information entropy. The suitable tree shape models are adjusted and selected by an automated training and testing process. The forecast model can classify rainfall with a forecast accuracy of more than 90% for strong rainfall over 30 mm.

The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student’s t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century.