Satellite-based Precipitation Products Research Articles

Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries

Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the effectiveness of the proposed model, we compare it with operational near-real-time satellite-based precipitation products and deep learning models which proved to be effective in precipitation estimation. We use classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR), Critical success index (CSI), and regression metrics including Root Mean Square Error (RMSE) and Pearson correlation coefficient (CC) to evaluate the performance of precipitation identification and precipitation amounts estimation, respectively. Furthermore, we select an extreme precipitation event to validate the generalization ability of our proposed model. Statistics and visualizations of the experimental results show the proposed model has better performance than operational precipitation products and baseline deep learning models in both precipitation identification and precipitation amounts estimation. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. This study suggests that applying an appropriate deep learning algorithm may provide an opportunity to improve the quality of satellite-based precipitation products.

Daily precipitation dataset at 0.1° for the Yarlung Zangbo River basin from 2001 to 2015

In order to obtain higher precision regional precipitation dataset in the Yarlung Zangbo River basin, two different schemes were proposed on the basis of the two most application potential satellite-based precipitation products, IMERG and CMORPH_BLD. The first method aimed to correct the positive error of IMERG based on high correlation (CC > 0.9) between IMERG and gauges. The second algorithm was developed to merge IMERG with CMORPH_BLD by the stepwise linear regression. As the reference, IMERG played a key role in correction of precipitation ratio determination and precipitation event detection. Two daily datasets with 0.1° resolution (BRD_IMERG and IGREA_IMERG-CMORPH) performed better than IMERG in CC, RMSE, ME, FAR and CSI, and streamflow simulation in the whole basin (NS: 0.86 and 0.87; RBIAS: −19% and −11%) and sub-basins. The two proposed methods were relatively simple and efficient for reconstructing higher precision regional precipitation, and the datasets provided a good application demonstration in the alpine region.

Impacts of Gauge Data Bias on the Performance Evaluation of Satellite-Based Precipitation Products in the Arid Region of Northwestern China

It has been reported that systematic bias exists in gauge measurements, which are usually used as the evaluation benchmark, so it is crucial to investigate the impacts of gauge data bias on the evaluation of satellite precipitation products. Six satellite precipitation products (IMERG, CMORPH, GSMaP, PERSIANN, PERSIANN−CCS, and PDIR−Now) and gauge data are collected from 2003 to 2015 in the arid region of Northwestern China. A daily correction for precipitation biases from wind-induced undercatch, wetting loss, and trace error is made for gauge measurements. The changes in metrics, including four continuous and four categorical metrics, are calculated to illustrate how the gauge data bias impacts the evaluation of six satellite precipitation products. The results show the following: The overall performances of six satellite precipitation products are undervalued by the gauge bias. Compared to other satellite products, the performance of IMERG is the best, whether before or after bias correction. However, the performances of all six satellite products are still not good enough even after bias correction and need to be improved. The impacts of gauge bias on the evaluation of the satellite precipitation products also differ by subregion, season, satellite precipitation product, precipitation intensity, and precipitation phase. In conclusion, the impacts of the gauge bias on the performance assessment of satellite products are obvious over the study region, implying that bias correction for gauge measurements is needed to obtain an accurate understanding of the performance of satellite precipitation products if choosing the gauge data as the evaluation benchmark.

Satellite-based meteorological drought indicator to support food security in Java Island.

A meteorological drought refers to reduced rainfall conditions and is a great challenge to food security. Information of a meteorological drought in advance is important for taking actions in anticipation of its effects, but this can be difficult for areas with limited or sparse ground observation data available. In this study, a meteorological drought indicator was approached by applying the Standardized Precipitation Index (SPI) to satellite-based precipitation products from multiple sources. The SPI based meteorological drought analysis was then applied to Java Island, in particular to the largest rice-producing districts of Indonesia. A comparison with ground observation data showed that the satellite products accurately described meteorological drought events in Java both spatially and temporally. Meteorological droughts of the eight largest rice-producing districts in Java were modulated by the natural variations in El Niño and a positive-phase Indian Ocean Dipole (IOD). The drought severity was found to be dependent on the intensity of El Niño and a positive-phase IOD that occurs simultaneously, while the duration seems to be modulated more by the positive-phase IOD. The results demonstrate the potential applicability of satellite-based precipitation monitoring to predicting meteorological drought conditions several months in advance and preparing for their effects.

Regional-scale evaluation of 14 satellite-based precipitation products in characterising extreme events and delineating rainfall thresholds for flood hazards

Gridded satellite-based rainfall products have not been so far evaluated for flood hazards monitoring through empirical methods, especially over large areas. Therefore, the main aims of this study are (i) to assess the quality of satellite-based precipitation products for identifying extreme rainfall events able to produce flood events, and (ii) to evaluate the use of satellite-based precipitation products for creating rainfall thresholds to support decision making for issuing flood warnings. Eight products fully based on satellite data (i.e., uncorrected) and six gauge-corrected products were evaluated based on ground-based data obtained from 583 sub-daily rainfall gauges and considering a catalogue of 551 flood occurrences in the state of São Paulo, Brazil, for a period of five years (2015–2019). The gauged values were compared with the precipitation products for six different time steps (3 h, 6 h, 12 h, 1 d, 3 d, and 10 d) and considering rainfall-duration thresholds for six non-exceedance probabilities (5%, 10%, 20%, 30%, 40%, and 50%). Results show that all analysed products tend to large underestimate the extreme rainfall events (i.e., when and where flood events were registered), mainly for sub-daily scales, with the best results found for two uncorrected products (i.e., PDIR-Now and GPM + SM2RAIN) considering 10-days accumulated precipitation. Considerable underestimations were also identified for the rainfall thresholds delineated by the satellite-based products, with the best performances obtained by CHIRP V2.0 (uncorrected) and IMERG-F (corrected) considering tolerance levels of 20%. Based on our findings, the rainfall satellite-based products dataset, even less accurate than the ground-based observations, can be applied, when multi-daily accumulated data are considered, as an alternative source of data for determining precipitation thresholds in some regions that present low-density of RAIN gauges but not replace the gauged data in regions with a high-density of rain gauges with sub-daily data available

Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and two error decomposition schemes are used to quantify their performance. Key results are as follows. (1) Except for V06UC’s relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB), and Northern China (HB) and CDR’s severe overestimation over TP, all four satellite-based precipitation products can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than over semi-humid, semi-arid, and arid regions. (2) All the statistical indicators show that CDR has the worst performance, whereas BLD is the best precipitation product. As for the two IMERG products, V06C has improved V06UC’s precipitation estimate. Results show that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of r, POD, and CSI. (3) Within all subregions, all four satellite-based precipitation products demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values among all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation products generally overestimate the frequency of moderate precipitation and light precipitation events (<25 mm/day) and underestimate heavy precipitation events (>42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. In addition, for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter.

Comparison of the Performance of IMERG Products and Interpolation-Based Precipitation Estimates in the Middle Reaches of Yellow River Basin

Accurate spatial precipitation data are of prime importance for hydrological simulations and flood forecasts. Interpolation methods and satellite-based precipitation products (SPPs) are often applied to obtain spatially distributed precipitation over basins. The objective of this study is to investigate whether satellite precipitation data can yield better estimates than the precipitation information (gauge observations) already available in the basin. In this study, we assessed the performance of three Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products against two calculated interpolation data and the ground precipitation observations at a daily scale in the middle reaches of the Yellow River Basin (MRYRB). This research includes two interpolation methods, namely, inverse distance weighting (IDW) and ordinary kriging (OK), and three latest IMERG SPPs, namely, IMERG “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F). The results show that the two interpolation methods (IDW and OK) obtain the best overall performance, followed by IMERG-F, whereas IMERG-E and IMERG-L have inferior performance. Compared with the two interpolation methods, IMERG-F obtains higher CC values and lower FAR scores during the rainy season and presents better correlation with the gauge-based precipitation at the basin boundary. Furthermore, IMERG-F possesses a better capability over IDW and OK in detecting heavy precipitation events (over 20 mm). Nevertheless, the three IMERG SPPs generally provide similarly poor performance in terms of detection metrics and failed to accurately detect winter precipitation. The findings of this study are expected to provide SPP researchers and users with useful feedback on the net utility of satellite products and remind researchers of the importance of interpolated precipitation data in the assessment of satellite precipitation.

How China’s Fengyun Satellite Precipitation Product Compares with Other Mainstream Satellite Precipitation Products

AbstractSatellite-based and reanalysis precipitation estimates are an alternative and important supplement to rain gauge data. However, performance of China’s Fengyun (FY) satellite precipitation product and how it compares with other mainstream satellite and reanalysis precipitation products over China remain largely unknown. Here five satellite-based precipitation products (i.e., FY-2 precipitation product, IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and one reanalysis product (i.e., ERA5) are intercompared and evaluated based on in situ daily precipitation measurements over mainland China during 2007–17. Results show that the performance of these precipitation products varies with regions and seasons, with better statistical metrics over wet regions and during warm seasons. The infrared–microwave combined precipitation [i.e., IMERG, GSMaP, and CMORPH, with median KGE (Kling–Gupta efficiency) values of 0.53, 0.52, 0.59, respectively] reveals better performance than the infrared-based only product (i.e., PERSIANN-CDR, with a median KGE of 0.31) and the reanalysis product (i.e., ERA5, with a median KGE of 0.43). IMERG performs well in retrieving precipitation intensity and occurrence over China, while GSMaP performs well in the middle to low reaches of the Yangtze River basin but poorly over sparsely gauged regions, e.g., Xinjiang in northwest China and the Tibetan Plateau. CMORPH performs well over most regions and has a greater ability to detect precipitation events than GSMaP. The FY-2 precipitation product can capture the overall spatial distribution of precipitation in terms of both precipitation intensity and occurrence (median KGE and CSI of 0.54 and 0.55), and shows better performance than other satellite precipitation products in winter and over sparsely gauged regions. Annual precipitation from different products is generally consistent, though underestimation exists in the FY-2 precipitation product during 2015–17.Significance StatementIntercomparison between the FY-2 precipitation product and mainstream precipitation products is valuable to guide applications of satellite precipitation products to China and its subregions. This study illustrates uncertainties in various satellite precipitation products, and could guide optimization of algorithms of precipitation retrieval and data fusion/merging to improve the accuracy and resolution of satellite precipitation products.

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.

Effective multi-satellite precipitation fusion procedure conditioned by gauge background fields over the Chinese mainland

The accurate estimation of precipitation has a basis for atmospheric and hydrological applications. However, large uncertainties exist in the estimation of regional precipitation derived solely from remote sensing data. Herein, we develop an effective method to fuse multiple satellite-based precipitation products to capitalize on their strengths and maximize their applicability at the 0.25° resolution scale. Specifically, we describe an approach based on bias correction in individual satellite-based products and their merging with up-scaled (1.0°) gauge-based interpolated data to produce finer -resolution (0.25°) data outputs. By focusing on seven regions to generate the estimates of merged precipitation across the Chinese mainland (MPCM), we performed a full evaluation based on a range of error metrics. The results showed that the bias correction procedure can reduce the discrepancies in the individual satellite-based precipitation products. In addition, the mean absolute error values are also considerably reduced (20%–45%) with respect to the original data over the Chinese mainland. Error statistical metrics demonstrated that the MPCM generated considerably better estimates on a daily scale, and performed better than the satellite-based precipitation products across different precipitation thresholds. The intercomparison results clearly states the superiority of the MPCM against Multi-Source Weighted-Ensemble Precipitation version 2 global product. Nonetheless, we also suggest that the large uncertainties in satellite-based products should be paid more attention over mountainous areas where rain gauge data are insufficient.

Evaluating the Performance of Satellite-Based Precipitation Products Using Gauge Measurement and Hydrological Modeling: A Case Study in a Dry Basin of Northwest China

AbstractSatellite-based precipitation products are commonly evaluated using gauge measurement, yet their regional evaluation and hydrological applicability have not been sufficiently studied, especially for dry basins. In this study, we evaluated the performance of four state-of-the-art remotely sensed precipitation products (CMORPH, GSMaP, IMERG, and PERSIANN-CDR) and their ensemble products (the reliability ensemble averaging and three-cornered hat methods) over the Heihe River basin, northwest China. Both direct evaluation using gauge measurement during 2001–19 and indirect evaluation using the Soil and Water Assessment Tool (SWAT) model during 2001–10 were conducted. Our results showed that 1) for point-to-pixel evaluation, GSMaP and IMERG products with high spatial resolution effectively captured the quantile distribution of gauge data; 2) compared to the spatially interpolated gauge data, all products underestimated the precipitation, among which GSMaP provided the closest interannual variability to the observations; 3) these products had better detection abilities upstream and during the rainy season, indicating that their performance was affected by the rain intensity—in particular, GSMaP exhibited the best ability; 4) the spatial patterns of individual products were inconsistent, while the ensemble products could reduce the bias with the gauge data; and 5) for hydrological modeling, streamflow simulation driven by GSMaP had the best performance, and the ensemble precipitation using the three-cornered hat method was better than that using the reliability ensemble averaging method. Collectively, these findings illustrated the reliability of GSMaP in representing the precipitation characteristics in similar arid areas and elucidated the advantages of using the three-cornered hat method.

Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

Spatially continuous assessment of satellite-based precipitation products using triple collocation approach and discrete gauge observations via geographically weighted regression

Assessment of the accuracy of satellite-based precipitation products (SPPs) is essential prior to their use in formal applications. However, thorough assessments of SPPs by traditional approaches are typically not practical when gauge observations are sparse. The triple collocation (TC) approach can be used to perform spatially continuous assessments, but TC is affected by error and bias. In this study, a novel approach for the spatially continuous assessment of SPPs that integrates TC and the traditional discrete gauge-based approach via geographically weighted regression (GWR) was developed. This approach, referred to as TC-GWR, allows SPPs to be assessed in areas with limited gauge observations. The TC-GWR was tested comprehensively through using four widely-used SPPs over mainland China. The TC-GWR generates spatially continuous assessment results for SPPs and outperforms significantly the original TC approach in assessing the accuracy of SPPs quantitatively. The performance of TC-GWR was generally robust to variation in the density of gauge stations (50, 100, 200, 400, and 800 gauge stations) and showed acceptable performance when gauge data are relatively sparse, except for the extremely sparse gauge cases (only 10 stations over mainland China). Overall, TC-GWR integrates the advantages of the traditional approach (higher reliability) and the TC approach (spatial continuity) and provides a valuable alternative for assessing SPPs in areas with limited gauge observations, such as remote mountainous areas and developing countries.

Hydrological evaluation of 14 satellite-based, gauge-based and reanalysis precipitation products in a data-scarce mountainous catchment

ABSTRACT Availability of high-quality data is a major problem for climate and hydrological studies, especially in basins with complex topography where gauge network is typically limited and unevenly distributed. This study investigates the performance of 14 precipitation products – seven satellite-based (SPPs), two gauge-based (GPPs) and five reanalysis products (RPPs) – against ground observations (1998–2007) in the transboundary Jhelum River basin (33 397 km2). Among the seven SPPs (bias corrected), five demonstrate a significantly high correlation coefficient (CC > 0.7) with observed rainfall. However, most of the products tend to underestimate the seasonal precipitation amount, particularly in winter and spring. Likewise, Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of water resources APHRODITE (GPPs) and Japanese 55-year Reanalysis JRA-55 (RPPs) are the best-performing products in daily streamflow predictions, with Nash-Sutcliffe efficiency values of 0.68 and 0.62, whilst MSWEP (Multi-Source Weighted-Ensemble Precipitation), AgMERRA (Climate Forcing Dataset for Agricultural Modeling) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) have also good potential in flow prediction. Generally, our results indicate that APHRODITE and JRA-55 could be used as alternative sources of precipitation data in the Himalayas region.

Evaluation and comparison of four satellite-based precipitation products over the upper Tana River Basin

Evaluation and comparison of four satellite-based precipitation products over the upper Tana River Basin

Adaptation of satellite-based precipitation product to study runoff and sediment of Indian River watersheds

Adaptation of satellite-based precipitation product to study runoff and sediment of Indian River watersheds

Variation in the response of ground and satellite-based precipitation products in the interpretation of drought in Tumakuru District, Karnataka

The unpredictable weather phenomenon is a cause for major concern as it brings along with it a series of difficulties. This draws our attention to drought which is one of the reasons of concern and needs to be dealt with in an effective manner. Although the rain gauge stations with a resolution of 250 x 250 provide precipitation data, the emerging satellite products show bigger promise. The valuation of Tropical Rainfall Measuring Mission (TRMM) data with the gauge data for 11 stations was conducted over the semi-arid region of Karnataka by means of data comparison and the drought indices such as Percent of Normal (PN), Modified China Z Index (MCZI), and Rainfall Anomaly Index (RAI). Comparative analysis was carried out for the monthly data using various statistical methods from the year 1998 to 2019. The study focuses on determining the feasibility and reliability of precipitation products in the analysis of drought. There is about 27.13% of similarity amongst the dataset for RAI while only 10.88% of data match for PN index. It was found that the satellite data is most suitable for the large-scale area while there are uncertainties when it comes to regional studies. The outcome of the result aims to provide a qualitative means to carry out drought-related studied and come up with beneficial solutions.

Hydrometeorological Modeling of Limpopo River Basin in Mozambique with TOPMODEL and Remote Sensing

The Limpopo River basin (LRB) is known for its vulnerability to floods, high rates of evapotranspiration, and droughts that cause significant losses to the local community. The present study aimed to perform simulations of flood events occurring in two Mozambican sub-basins of LRB, namely Chókwè and Xai-Xai from 2000 to 2015 with TOPography-based hydrological MODEL (TOPMODEL) and satellite remote sensing data. As input in TOPMODEL, data from two high-resolution global satellite-based precipitation products: Climate Prediction Center MORPHing technique (CMORPH) and Integrated Multi-Satellite Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), 8-day MOD16 evapotranspiration product and surface runoff data estimated by Global Land Data Assimilation System (GLDAS) were used. The sensitivity tests of TOPMODEL parameters were applied using the Monte Carlo simulation. Calibration and validation of the model were performed by the Shuffled Complex Evolution (SCE-UA) method and were evaluated with the Kling-Gupta Efficiency (KGE) index. The results indicated that simulations with the GPM-IMERG (KGE: 0.59 and 0.65) tended to underestimate the stream flows, while with the CMORPH product the performance was much better (KGE: 0.66 and 0.77) in both sub-basins. Thus, TOPMODEL can help to develop flood monitoring systems from satellite remotely sensed data in similar regions of Mozambique.

Performance of satellite-based and reanalysis precipitation products under multi-temporal scales and extreme weather in mainland China

Gridded precipitation products are efficient supplements to surface meteorological gauges and thus a comprehensive evaluation of such products is essential. This study systematically evaluated the performance of 4 gridded precipitation datasets with hourly scale in mainland China, with ground gauge precipitation as a benchmark. In particular, we assessed the performance of two satellite-based precipitation estimations (SPEs): Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP), and two reanalysis precipitation estimations (RPEs): Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA) and Climate Forecast System Reanalysis (CFSR). The results reveal that all the products have better performance in low-latitude areas, and have worse performance in the high-latitude mountainous regions. SPE products have similar features of temporal-spatial performance and diurnal cycle estimation, and so have RPE products. The overall summer SPE accuracy surpasses that of all RPE products, and vice versa in winter. The error decomposition of each product linked the summer bias to excessive false events of light precipitation. In winter, the SPE error is generally a result of missed and false events, while that of the RPEs originates from false precipitation. The product accuracy increases with the time scale, and the daily MERRA accuracy exceeds that of other products. However, due to the challenge in representing convective precipitation, the accuracy of the diurnal cycle and peak hour is lower than that of the SPEs, and representing weaker amplitudes compared to the gauge precipitation. This consequently results in RPEs’ poorer performance in extreme heavy precipitation compared to the SPE products. The results of this study are useful for both developers and users of gridded precipitation products.

Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China

Accurate precipitation retrievals with fine spatio-temporal resolutions are critical in global and regional analyses. As an important alternative of satellite-based precipitation products, model-based precipitation estimates have undergone rapid development over the past few decades. With the recent public release of the fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5) and ERA5-Land, it is necessary to verify whether these two latest model-based precipitation products outperform satellite-based precipitation products. This study comprehensively evaluates the performances of state-of-the-art satellite-based and model-based precipitation products, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), ERA5, and ERA5-Land, over mainland China from 2016 to 2019. The main findings are as follows: (1) Satellite-based products generally outperform model-based products, but the latter significantly perform better than the former over high-latitude regions and in winter. (2) ERA5 and ERA5-Land share similar spatio-temporal patterns and have their own advantages in terms of different types of metrics. (3) Satellite-based products perform best over subregions of subtropical and tropical monsoon climate (ST), whereas model-based products show highest performance over subregions of temperate monsoon climate (TM) and temperate continental climate (TC); both types of products show the poorest performance over subregions of plateau mountain climate (PM). (4) IMERG-Final performs best in terms of precipitation events, while GSMaP-Gauge tends to overestimate the duration of precipitation events, and model-based products tend to underestimate the mean precipitation rate of the events. These findings provide valuable insights into the error characteristics of state-of-the-art model-based and satellite-based precipitation products in the recent years, and will also serve as useful reference for the potential improvement of precipitation retrieval algorithms in the next generation.