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The spatial distribution of precipitation in Xinjiang, China, is extremely heterogeneous due to its complex subsurface, and microphysical studies of cloud precipitation over Xinjiang are severely constrained by observational means. The Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR) provides improved three‐dimensional observation data of strong and weak precipitation and performs effective observations in alpine mountain regions. Therefore, this study used GPM/DPR Level‐3 data from January 2015 to December 2022 to analyze the convective and stratiform precipitation characteristics over Xinjiang. The results showed that (1) the range of maximum precipitation reflectivity factors (PRF_max) near the ground and at different heights were larger in summer and maximum PRF_max values appeared in areas along the Altai, Tianshan, and Kunlun Mountains and plains on the respective mountain slopes. In summer, the PRF_max of stratiform (convective) precipitation nearsurface ranged from 24 to 34 dBZ (28–40 dBZ). (2) The two types of precipitation exhibited the highest storm top heights in summer, with convective precipitation having higher values than stratiform precipitation and mountainous areas having higher values than the surrounding basins due to the effects of topographic uplift. (3) The value domains of the PRF, particle mass‐weighted diameter (Dm), and particle number concentration for stratiform (convective) precipitation were more concentrated (wider), that is, PRF: 14–30 dBZ (12–40 dBZ), Dm: 0.9–1.4 mm (0.7–2.1 mm), and the particle concentration: 29–35 (24–39). The height of the center of the probability density of each variable was higher in the three major mountain ranges than in the two major basins owing to the influence of topography. PRF increased and then decreased with height. Convective raindrops were in an active state of high concentration and exhibited a large scale in the lower layers, whereas the particle spectral parameters of stratiform precipitation showed a more concentrated and symmetric distribution and insignificant changes with height. [ABSTRACT FROM AUTHOR]

In the future, as a result of global warming, it is possible that rainfall could become more intense and frequent. This could lead to more frequent triggering of damaging phenomena such as floods and landslides (named as a whole damaging hydrogeological events, DHE), and, consequently, to the increase of their impacts on territories, especially in regions where uncontrolled urban sprawl represents a factor that can exacerbate the problem. The analysis of a large quantity of information about both triggering rainfall and triggered phenomena can help to comprehend relationships between triggering precipitation and its related impacts. In this paper, to facilitate the investigation of the relationships between large and complex datasets concerning both rainfall and rainfall-related damage, we propose an index-based approach, illustrated by its application to the Calabria region (Southern Italy). In particular, this manuscript presents some results from a preliminary investigation aimed at assessing the "better" index to describe DHE. Five rainfall indices (RIs) were tested and five composite rainfall indices (CRIs), combinations of two or more RIs, are proposed. We calculated the RIs and the CRIs by means of 1,300,000 daily data registered in the observation period 1980–2020. The CRIs showed the best relationships with the data of damaging hydrogeological events (DHEs). Particularly, better results were obtained with landslides data than with floods data, perhaps due to the hydraulic characteristics of the Calabria rivers, affected by flash floods mainly influenced by very intense hourly rainfall events. [ABSTRACT FROM AUTHOR]

Nowadays, the Mediterranean region is generally recognized as a climate change hot spot given its strong response to global warming, with relevant impacts on rainfall amount and distribution. Within this context, in this work the temporal variability of rainfall at annual, seasonal and monthly scale was analyzed in Italy using rainfall data extracted from the reanalysis dataset ERA5-Land during the period 1950–2020. In particular, rainfall trend magnitude and significance have been estimated by means of non-parametric tests applied to 3215 grid points falling within the Italian territory. The main results of this analysis evidenced only a few relevant trends at the annual scale, mostly involving northern Italy (positive trend) and the Sardinia region (negative trend). At seasonal scale, the results showed a marked negative trend in winter, characterizing almost all the Italian territory, while in the other seasons a positive trend was identified in the majority of grid points, especially in the Alps. Finally, at the monthly scale, September was identified as the month of the year with the highest percentage of grid points with positive trends mainly located in central, southern, and north-eastern Italy. [ABSTRACT FROM AUTHOR]