Understanding how severe hailstorms will respond to climate change remains challenging partially due to an incomplete understanding of how different environments produce hail. Leveraging a record of 14,297 global potential severe hailstorms detected by spaceborne precipitation radar, here for the first time, we explore global differences in the five distinct environmental types producing these storms. Two are found over tropical plains and hills with high convective instability, high‐moderate moisture, and low vertical wind shear (VWS). The third type are supercell environments characterized by strong VWS, with moderate instability and moisture, commonly occurring over mid‐latitude plains. Higher latitude plains and elevated terrain reflect the final two, with moderate VWS and low melting height, instability, and moisture. The variety of hailstorm environment types illustrates distinctions in the associated convective mode and embryo type, highlighting that multiple environment types pose challenges for modeling present frequency and anticipating the response of hail to climate change.

Hail size records are analyzed at 2254 stations in China and a hail size climatology is developed based on gridded hail observations for the period 1960–2015. It is found that the annual percentiles of hail size records changed sharply and national-wide after 1980, therefore two periods, 1960–79 and 1980–2015, are studied. There are some similarities between the two periods in terms of the characteristics of hail size such as the spatial distribution patterns of mean annual maximum hail size and occurrence week of annual maximum hail size. The 1980–2015 period had higher observation density than the 1960–79 period, but showed smaller mean annual maximum hail size, especially in northern China. In the majority of grid boxes, the annual maximum hail size experienced a decreasing trend during the 1980–2015 period. A Gumbel extreme value model is fitted to each grid box to estimate the return periods of maximum hail size. The scale and location parameter of the fitted Gumbel distributions are higher in eastern China than in western China, thereby reflecting a greater likelihood of large hail in eastern China. In southern China, the maximum hail size exceeds 127 mm for a 10-yr return period, whereas in northern China maximum hail size exceeds this threshold for a 50-yr return period. The Gumbel model is found to potentially underestimate the maximum hail size for certain return periods, but provides a more informed picture of the spatial distribution of extreme hail size and the regional differences.

The response of hailstorm intensity to climate variability/change has become a topic of community interest recently. But the lack of persistent and homogenous observations makes it difficult to confidently describe its interannual variations. Hail size, as a common indicator of hailstorm intensity, displays distinct regional variability. Here, for the first time, we show robust evidence of a decrease in hail size using continuous and coherent hail size records from 2,254 manned stations in China since 1980. The stations were categorized based on their elevation: plateaus (above 2000 m), foothills (between 500 and 2000 m), and plains (below 500 m). Compared with 1980–1997, the hail size spectra from 1998 to 2015 all shifted toward smaller sizes significantly in plateaus, foothills, and plains. The proportion of overall hail events with maximum sizes of at least 5 and 20 mm significantly decreased since 1980. Meanwhile, the annual mean size of hail above 10 and 20 mm significantly decreased during the research period, especially after 1990. These changes in the hail size spectra may imply a weakened intensity of hailstorms in China in recent decades.

The Lightning Mapping Imager (LMI) onboard Fengyun‐4A (FY‐4A) is the first Chinese lightning detection sensor in geostationary orbit. This study presents the lightning characteristics observed by the FY‐4A LMI in its first year, and then observations from the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS) are used to validate the performance of the LMI sensor. LMI lightning events are defined by the pixel's luminosity exceeding the threshold radiance. During the first year of operation, LMI events revealed a reasonable geographical distribution. The diurnal cycles of LMI event/background/threshold radiance are in general agreement with the later afternoon peaks in previous findings. However, the LMI events that occurred around the LIS flash time and in the LMI observation field‐of‐view show a slightly delayed peak at around 1900 LST. This peak can be shifted closer to LIS's results after removing LMI events with event radiance lower than 300 , suggesting that a larger threshold radiance value is needed to qualify some of these LMI events. Finally, LIS lightning groups were matched with LMI lightning events using different temporal and spatial collocation criteria. The detection efficiency is defined by the fraction of LIS groups detected by LMI to assess LMI's performance. The LMI detection efficiency relative to LIS was found to be regionally dependent and lower in the daytime than at nighttime. These results contribute to applications of LMI lightning observations in storm research and provide suggestions for further algorithm improvement.

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.