Abstract: Abstract: Rainfall erosivity is characterized by the potential capability of rainfall to cause soil loss in the regional soil and water conservation. It is a high demand to collect the rainfall data with high time resolution for the better evaluation of rainfall erosivity. In this study, multiple simple algorithms were extended to calculate the rainfall erosivity in the time series under certain accuracy requirements, further to meet the research needs of the temporal and spatial evolution of erosivity. The Xiaoanxi Basin in the Yangtze River was taken as the study area with frequent human activities and serious soil disturbance. First, the K-means clustering was used to categorize all the erosive rainfalls in the recent 10 years using the minute-by-minute rainfall data from Yongchuan, Dazu, Tongliang and Hechuan meteorological stations from 2009 to 2018. Second, an optimal simple algorithm was determined from six selected algorithms, according to the standard calculation on the classic rainfall erosivity. A long-sequence calculation of rainfall erosivity was then carried out for the daily rainfall data from 1960 to 2018. Third, a Mann-Kendall test was applied to analyze the rainfall erosivity characteristics from spatial and temporal aspects, using rescales range and inverse-distance-weighted spatial interpolation. The results showed that: 1) The percentage of annual mean rainfall, duration, intensity, frequency, and erosivity of erosive rainfall in the total rainfall were 71.45%, 38.06%, 52.93%, 15.11% and 96.30%, respectively. The erosive rainfall over the basin was divided into three categories as follows. Category I was characterized by the low rainfall, medium duration, low intensity with the least erosivity of 33.90 MJ·mm/(hm2·h), and the highest percentage of occurrences of the total erosive rainfall up to 87.43%. The categoryⅡof the erosive rainfall presented a higher erosivity of 330.12 MJ·mm/(hm2·h) and a frequency of 10.01%. The category Ⅲ belonged to the highly intensive rainfall with the most erosivity of 1 176.86 MJ·mm/(hm2·h), accounting for only 2.56% of the total occurrence times. 2) The simple algorithm B was illustrated as the optimized calculation for the long-sequence rainfall erosivity in the study area from 1960 to 2018. The multi-year average rainfall erosivity was among 2 037.14 to 2 464.71 MJ·mm/(hm2·h) in the study basin. The rainfall erosivity showed a decreasing trend in all selected stations, except Tongliang with an increase of about 3.66%. The Yongchuan station performed the best with the change range of 12.39% in terms of change magnitude. The annual variation of rainfall erosivity showed a bimodal distribution with the highest value in the first half of July. The period of high rainfall erosivity was from May to September, where the cumulative value accounted for 84%-88% of the annual rainfall erosivity. Therefore, it is necessary to strengthen the prevention and control of water and soil loss in this period. 3) In spatial distribution, the rainfall erosivity showed the increasing feature from the upstream to downstream. There was an outstanding spatial discrepancy of rainfall erosivity in different seasons. The potential risk of hydraulic erosion most likely appeared at downstream, where the Xiaoanxi River merges into the Fujiang River Basin.