Global warming weakened the summer monsoon and increased the evaporation, leading to more contribution of local evaporation moisture to the local precipitation for the monsoon areas. However, the descriptions of the contribution of the local moisture to the total precipitation and its characteristics have not been known very well. In this paper, taking the middle and lower Reaches of the Yangtze River (MLRYR) as a case and using the precipitation recycling process model, we analyzed the characteristics of the contribution of the local moisture to the total precipitation and the possible reasons. The results show that: the seasonal difference in precipitation recycling rates is obvious, the precipitation recycling rates in spring and summer are small (18.30% and 19.30%), the maximum in autumn is 30.50%, and the precipitation recycling rates in all seasons except summer show a significant upward trend (about 1.70%/10a). Additionally, the water vapor input into MLRYR from four boundaries significantly reduced except for the eastern boundary, and the water vapor contribution from the South and East borders is in summer, and the water vapor contribution from the North and West borders is in autumn, winter and spring. We suggest that the model of the precipitation recycling rate is important to evaluate the contribution of different water vapor sources, and help to further improve the ability of river water prediction in flood season.

Based on ERA5 from 1980 to 2018, we compare and analyze the trend and interdecadal variation of evaporation anomalies over the tropical Indian Ocean by the evaporation decomposition method. This method mainly decomposes the evaporation anomalies into the Newtonian cooling, stability, relative humidity, wind speed, and transfer coefficient terms. The annual mean evaporation anomalies show an increasing trend (0.083 mm/d/decade). The Newtonian cooling term (0.026 mm/d/decade), the relative humidity term (0.032 mm/d/decade), and the wind speed term (0.026 mm/d/decade) play a major role in the increasing trend. The interdecadal variation of evaporation anomalies shows decreases in the 1980s and after the early 2000s, and an increase in the 1990s. The decreased evaporation anomalies in the 1980s are affected by the transfer coefficient term, which is associated with the North Atlantic Oscillation (NAO). The increased evaporation anomalies in the 1990s and the decreased evaporation anomalies since the early 2000s are largely controlled by the wind speed term, which are dominated by the Atlantic Multidecadal Oscillation (AMO). The Pacific Decadal Oscillation (PDO) may have important impacts on the interdecadal increase of evaporation anomalies by affecting the wind speed in the 1990s.

Based on four datasets (ERA5, JRA-55, MERRA-2, and OAFlux) and the evaporation decomposition method, this study examines the principal spatiotemporal characteristics of North Atlantic evaporation during the cold season (December–May) and warm season (June–November) from 1980 to 2015. The results indicate that during the cold season, all four datasets consistently exhibit a meridional triple pattern, driven primarily by the wind speed term (U*) and the stability term (S*). All datasets reveal a synchronous interdecadal shift around the late 1990s, underscoring the high reliability of cold season evaporation features. In contrast, the dominant evaporation modes during the warm season diverge significantly across datasets. ERA5 and JRA-55 display a dominant zonal triple pattern, whereas this pattern emerges only as a secondary mode in MERRA-2 and OAFlux, with notable discrepancies in both spatial structure and temporal evolution. Warm season patterns are mainly controlled by the relative humidity term (RH*), and the associated uncertainties can be attributed to differences in how the various datasets characterize RH* under global warming. This study demonstrates that the cold season evaporation characteristics over the North Atlantic are highly credible, offering a robust foundation for mechanistic studies. Conversely, warm season results exhibit sensitivity to dataset selection, necessitating rigorous uncertainty assessment in future studies. Our findings provide a scientific basis for data selection and seasonal differential analysis in related research.