## 1 Introduction  
Precipitation has great significance in the study of ecology, hydrology and meteorology [1; 2], but it is still a great challenge to acquire the spatial and temporal distribution of precipitation over many developing countries and mountainous regions, due to the sparse rain gauge network. With the development of meteorological satellite networks, the information provided by space-borne sensors has become the main focus for estimating precipitation over un-gauged regions [3; 4]. In the past decades, various algorithms have been developed to retrieve precipitation from infrared and microwave satellite observations [5; 6; 7; 8; 9; 10]. However, considerable studies revealed that current satellite precipitation datasets contain enormous biases and random errors that could lead to serious uncertainty in many scientific applications [4; 11; 12]. [PERSON] _et al._[13] separated the bias of high-resolution satellite precipitation datasets into three independent components: misdetected bias (rain areas that were incorrectly determined as no rain areas by satellite precipitation datasets), false alarm bias (no rain areas that were incorrectly determined as rain areas by satellite precipitation datasets) and hit bias (the rain areas correctly determined by satellite precipitation datasets, but for which the precipitation volumes were inaccurately estimated). Furthermore, they indicated that the false alarm precipitation was a leading error source in many high-resolution satellite precipitation datasets. [PERSON] _et al._[14] revealed that more than 50% of the rainfall biases were caused by misdetected precipitation during winter and that 28% were caused by false alarm precipitation. [PERSON] and [PERSON] [15] pointed out that the uncertainty in the Climate Prediction Center morphing (CMORPH) and Tropical Rainfall Measuring Mission 3B42 was governed by misdetected precipitation and hit bias. From these studies, it can be inferred that a great part of uncertainty in satellite estimates is governed by misclassified rain/no rain areas. Consequently, developing a more accurate rain area delineation method is of great significance for improving the performance of high-resolution satellite precipitation datasets. Benefitting from the better penetrability of microwave radiation, space-borne microwave sensors can provide information on the horizontal and vertical distribution of hydrometeors inside the cloud system [16]. Therefore, retrieving precipitation information from microwave observations has attracted considerable attention for both global and regional applications [17; 18]. Since the 1980s, several methods have been developed to delineate rain areas on the basis of multi-channel passive microwave observations. [PERSON] [19] proposed the polarization-corrected temperature (PCT) method using the polarization difference between the land surface and hydrometeors in order to identify the rain pixels in passive microwave TBs(brightness temperatures) images. [PERSON] [20] and [PERSON] [21] developed the Scatter Index at 85 and 37 GHz to measure the scattering intensity of clouds and eventually retrieved global rain areas with a threshold. [PERSON] _et al._[22] introduced the dynamic cluster K-means method to delineate rain areas over Tibetan Plateau using multi-channel microwave brightness temperatures. Moreover, several threshold methods based on brightness temperatures at passive microwave (PMW) channels have been proposed to separate rain areas from land surface [23, 24, 25]. However, due to the spatially and temporally varying relationship between the occurrence of precipitation and the brightness temperatures at PMW channels, these methods usually cannot identify the rain/no rain pixels accurately. Artificial neural networks (ANNs) are a non-parametric intelligent tool that is useful for non-linear fitting, pattern recognition and process control [26]. Since the 1990s, several types of ANN have been developed to retrieve rain information from satellite observations.