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Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

Both long‐term but small number of national stations and short‐term but large number of regional stations have been frequently used to study the extreme hourly precipitation (EXHP) in China. However, few studies focus on the differences of the two for revealing the features of EXHP. In this study, the characteristics of EXHP in Zhejiang Province are investigated using three rainfall data sets at three threshold criteria. The comparison between different data sets shows that increasing the station density can better reflect the climatic spatial distribution of EXHP thresholds if long‐term data is absent. The majority of EXHP can be classified into four weather types: the southwesterly wind type (30.7%–48.5%), the trough type (12.2%–23.6%), the tropical cyclone (TC) type (11.4%–17.5%) and the easterly wind type (4.9%–17.9%). The selection of stations is more sensitive to the proportions of the four weather types than the statistical years and threshold criteria. The monthly and diurnal variations of EXHP, as well as their differences revealed by the three data sets, are varied by weather type. Only using national stations cannot distinguish the spatial differences between the TC type and the easterly wind type, and there is an underestimation for southwesterly wind type and trough type in the mountainous area of southwest Zhejiang. The statistical year and station height are the main reasons for the differences in the duration of EXHP events calculated by different data sets, with the TC type having the largest effect and the southwesterly wind type the smallest. Plain Language Summary: Station data is the most common and reliable data for studying the characteristics of precipitation in China. There are two sources of station data available, that is, national station data with long time series but sparse and regional stations with short time series but numerous. However, few studies have compared the differences between these two sources of data for revealing the characteristics of extreme hourly precipitation (EXHP). In this study, the possible impact of different data sets for revealing the characteristics of EXHP under four weather types are investigated in Zhejiang Province. Zhejiang is located on the southeast coast of China with a long coastline and many mountains. Results suggest that increasing the station density can better reflect the spatial distribution of the EXHP thresholds if long‐term data is absent. EXHP events with strong synoptic‐scale forcing are more sensitive to the results obtained by different data sets. As the national stations are mostly located in the plains, the high frequency centers of EXHP are biased in each weather type if only using national stations, especially in southwest Zhejiang and near the coast. In summary, this paper provides additional perspectives for understanding the characteristics of regional EXHP by using the most common observations. Key Points: The selection of stations is sensitive to the proportions of the four main weather types in Zhejiang where extreme hourly rainfall occursExtreme hourly precipitation events with strong synoptic‐scale forcing are more sensitive to results obtained using different data setsThe sparse national stations located on the plains could hardly reflect the spatial distribution of extreme rainfall in mountainous Zhejiang [ABSTRACT FROM AUTHOR]