The study on the temporal and spatial distribution characteristics of hourly precipitation and short-time heavy precipitation in Jiangxi Province can provide the climate change background of precipitation and short-time heavy precipitation, and lay a foundation for further application. Based on the hourly precipitation data from 86 national meteorological stations of Jiangxi Province from March to November during 1979-2019, the diurnal change characteristic of ratio and frequency of hourly precipitation and short-time heavy precipitation in Jiangxi Province were analyzed. The results are as follows: (1) The diurnal variation of ratio and frequency of hourly precipitation and short-time heavy precipitation in Jiangxi Province shows an obvious bimodal characteristics. One of the peaks occurs from 15:00 BST to 20:00 BST and another occurs from 05:00 BST to 10:00 BST. (2) The daily maximum values of ratio and frequency of hourly precipitation and short-time heavy precipitation are high in the south and low in the north of Jiangxi Province. The spatial distributions of daily minimum value of ratio and frequency of hourly precipitation and short-time heavy precipitation are complementary to the daily maximum value. The daily maximum value of hourly precipitation ratio in most areas of Jiangxi Province appears from 15:00 BST to 20:00 BST, while the daily minimum value mainly appears from 20:00 BST to 03:00 BST the next day. (3) The diurnal distribution of precipitation ratio in southern Jiangxi presents single peak pattern, the maximum value appears from 15:00 BST to 20:00 BST, while that in northern meteorological stations are mainly double peak pattern, the first peak appears from 05:00 BST to 10:00 BST and another peak appears from 15:00 BST to 20:00 BST, but it at meteorological stations around the Poyang Lake is single peak pattern, and the maximum value appears from 05:00 BST to 10:00 BST. (4) The diurnal variations of ratio and frequency of precipitation in different seasons are slightly different. The bimodal structure of ratio and frequency of precipitation began in late-April, but the precipitation ratio ended in early-June and the precipitation frequency ended in mid-June. The unimodal structures are dominant in other periods. The peaks from March to mid-April and from late-September to November appear from 00:00 BST to 10:00 BST and 05:00 BST to 10:00 BST, respectively, while from late-June to mid-September they mainly appear from 15:00 BST to 22:00 BST. The diurnal variations of ratio and frequency of short-time heavy precipitation are unimodal structure from March to early-April, and the peaks mainly occur from 00:00 BST to 10:00 BST. From mid-April to mid-July, they are mainly bimodal structure, and the peaks mainly occur from 05:00 BST to 10:00 BST and 15:00 BST to 20:00 BST. From late-July to mid-September, they turn into a single peak pattern, and the peaks mainly occur from 15:00 BST to 20:00 BST.

With continuous growth of forecast service demand and increasingly refined forecast content, the forecast of short-term heavy precipitation above 20 mm·h^-1 can not meet the forecast service demand fully. It is very necessary to carry out research on forecast methods about short-term heavy precipitation with different rainfall intensity. The 51 355 samples of short-term heavy rainfall from national and regional meteorological stations in nine provinces and one city in southern China from June to August during 2016-2019 were divided into four rainfall grades according to their rainfall intensity (R), namely, I: 20≤R<30 mm·h^-1, II: 30≤R<50 mm·h^-1, III: 50≤R<80 mm·h^-1, and IV: R≥80 mm·h^-1. The samples of all rainfall grades were spatiotemporal matched with the initial field of CMA-MESO (China Meteorological Administration mesoscale model) in the same period, and the percentile statistics were applied to 22 physical quantities extracted from these samples. The XGBoost (extreme gradient boosting) machine learning method was used to rank importance of those 22 physical quantities to determine their weight coefficients. Based on the continuous probability prediction method, the ascending and descending half ridge functions were selected as the membership function, the probability prediction models of short-term heavy precipitation with different rainfall grades were established. The real-time operational prediction was carried out in flood season of 2020 using these prediction medels, and the hourly probability prediction products of short-term heavy precipitation with different rainfall grades for 0-36 h prediction time during 15 heavy rainstorm precesses in Hubei Province from June to August 2020 were tested. The results show that for the grade I probability prediction products, the TS score (0.145) using 60% as threshold works best, with a corresponding hit rate of 55.7%; for the grade II probability prediction products, the TS score (0.083) using 65% as threshold works best, with a corresponding hit rate of 39.1%; for the grade III probability prediction products, the TS score (0.03) using 70% as threshold works best, with a corresponding hit rate of 21.7%; for the grade IV probability prediction products, the TS score (0.005) using 80% as threshold works best, with a corresponding hit rate of 5.8%.The results also suggest that probability prediction products help to correct the CMA-MESO model in predicting short-term heavy precipitation with different rainfall grades at the same time. The hourly prediction test of three heavy precipitation processes shows the hit rate of 40%-80%, the false rate of 50%-90%, and 36 h prediction time for the grade I probability forecast products, which are generally better than CMA-MESO precipitation forecast at the same time. A model was established to forecast short-term heavy precipitation with different grades in this study, and it outperforms existing numerical models and can be a good reference for meteorologists to forecast short-term heavy precipitation and correct precipitation forecast biases in CMA-MESO.