While many studies have compared global precipitation datasets at national, continental, and global scales, few have evaluated these data at river basin scales. This study explored differences in precipitation estimates and trends of 12 widely applied precipitation datasets, including gauge-, satellite-, and reanalysis-based products, for the world's 6,292 river basins. Results showed that disparities between 12 precipitation datasets were considerable. A total of 3,125 river basins, with a land area of 5,989.1×104 km2, had differences in estimated annual average precipitation exceeding 500 mm yr−1, and these basins were mainly distributed in Greenland, Africa, Oceania, and West Asia. Disparities between the precipitation datasets were particularly large during the dry season when the percentage difference between the highest and lowest precipitation estimates exceeded 500% in 1,390 river basins (4,839.7×104 km2) expected due to numerical reasons. Differences in rainfall trends also varied markedly between data sources. The data products do not agree on precipitation trends for all river basins. These findings illustrate the importance of accurate precipitation data to ensure effective policy and planning in term of hydropower generation, domestic water supply, flood protection, and drought relief at river basin scales and highlight the uncertainty that exists in current global precipitation data.

  • A comprehensive evaluation of global precipitation datasets was compared at the river basin scale.

  • Seasonal and annual differences in these precipitation datasets were compared.

  • Gives a sufficient explanation of which product is suitable for which river basin.

The intensification of climate change has accelerated the global water cycle, causing significant spatiotemporal heterogeneity in global precipitation (Los et al. 2001; Siepielski et al. 2017; Roushangar et al. 2018; Chai et al. 2019, 2020, 2021, 2022; Zhu et al. 2023). Accurate and reliable precipitation data can reveal the mechanisms driving precipitation change, hydraulic performance of flow confluence characteristics, and the accuracy of hydrological, ecological, and atmospheric models depends heavily on the availability of good-quality precipitation estimates (Huffman et al. 1997; Bagley et al. 2014; Espinoza et al. 2016; Roushangar et al. 2020a, 2020b).

Currently, many precipitation data products based on observations exist, including gauge-, satellite-, and reanalysis-based products. Tools, including rain gauges, disdrometers, and radar are widely used to monitor precipitation near the Earth's surface, with the advantage of high precision at gauge locations (Sun et al. 2018). Gauge observations have been widely used to measure precipitation directly at the Earth's surface (Kidd 2010). However, gauge-based products have certain disadvantages: uneven distribution of gauge stations; gauge scarcity in deserts, oceans, forests, mountains, valleys, and developing countries; and a lack of long-term and continuous observations (Easterling et al. 1996; Ringard et al. 2015). Satellite-based precipitation products can overcome these deficiencies by providing precipitation fields with high space–time variability and global coverage (Thiemig et al. 2012; Derin & Yilmaz 2014). Therefore, satellite precipitation products have become essential sources of precipitation information, especially in regions where the gauged distribution is sparse and uneven (Derin & Yilmaz 2014). Satellite data have been used widely to forecast typhoons, flooding, and heavy rain, and in the analysis of atmospheric circulation characteristics (Olson et al. 1996; Liu et al. 2011). This kind of dataset has also been used in water resources management and drought monitoring (Li et al. 2018; Amini et al. 2019). However, it has been suggested that satellite-based precipitation products may include large systematic and random errors (e.g., observation errors, sample source uncertainty, and algorithm errors), especially in the mid-to-high latitudes and areas of complex terrain (Xie & Arkin 1996; Nijssen & Lettenmaier 2004; Dinku et al. 2008; Villarini et al. 2009; Pan et al. 2010). The complex terrain, with varied cloud cover, may disturb satellites to capture a more accurate estimate of precipitation (Villarini et al. 2009; Pan et al. 2010). Reanalysis precipitation products are obtained by reintegrating and optimizing various observation data (Bengtsson & Shukla 1988). In detail, reanalysis provides the most complete picture currently possible of past weather and climate, and they are a blend of observations with past short-range weather forecasts rerun with modern weather forecasting models. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Owing to their dynamic mechanisms and physical characteristics, reanalysis products have the advantages of wide spatial coverage, long-term data availability, continuity, and consistency (Masina et al. 2011; Bellucci et al. 2013). Notwithstanding, reanalysis data are still affected by observation uncertainty, model, and assimilation errors (Karam & Bras 2008; Thorne & Vose 2010).

Before selecting and applying a precipitation product, it is necessary to evaluate the differences between the available data sources. In recent decades, researchers have focused on the inter-comparison of precipitation datasets at national, continental, and global scales (Adler et al. 2001; Tian et al. 2009; Liu et al. 2010, 2011; Gehne et al. 2016; Herold et al. 2017; Tang et al. 2018; Li et al. 2019; Zhang et al. 2019). For example, Sun et al. (2018) quantified the discrepancies between 30 precipitation products over multiple time scales and found that the difference in annual precipitation over the land surface could be as high as 300 mm year−1 (Sun et al. 2018). At the river basin scale, the reliable estimate of precipitation is closely related to the local water resources management, flood control and disaster reduction and reservoir operation. However, few studies have considered data inter-comparison at the watershed scale. In particular, a comprehensive evaluation of the available precipitation data for each river basin worldwide has not yet been attempted.

In the past five years alone, thousands of studies have applied different precipitation products to address scientific problems on the catchment scale (Kling et al. 2014). For instance, both gauge-based (Chen et al. 2014; Zhang et al. 2015) and reanalysis precipitation data (Su et al. 2017) have been used in the Yangtze River Basin to investigate the effects of precipitation on river discharge. There exist certain differences in the estimation of precipitation in the Yangtze River Basin with different precipitation products, thus it is crucial to select the appropriate precipitation products. Considering the wide application of different precipitation products at the catchment scale, a comprehensive evaluation of these products at the river basin is warranted. Hence, this study aims to identify and quantify the spatiotemporal heterogeneities of 12 different precipitation products, including gauge-, satellite-, and reanalysis-based products. To do this, we use the TFPM-MK Method and the Maximum Percentage Difference (MPD) Method to evaluate the magnitude of the difference in estimating multi-year averaged values of the precipitation at seasonal scale (dry season and wet season) and at annual scale, and in estimating the trends of precipitation, in 6,292 river basins.

Methods

In this paper, we used 12 precipitation data products, including gauge-, satellite-, and reanalysis-based products, to evaluate the performance of the different products. To do this, we calculate the values of annual mean precipitation, and mean precipitation in dry and flood seasons, by using Thiessen Polygons method. We further analyze the trends of precipitation in each river basin (TFPM-MK Method), and estimated the MPD of the annual precipitation across the products. To evaluate the difference in estimating precipitation, we have the bilinear interpolation method to transform all the precipitation products into the spatial resolution of 0.5° × 0.5°.

Thiessen Polygons method

The Thiessen Polygons method has been widely used in hydrometeorology, geoscience, and environmental science (Sharma et al. 2007; Huang et al. 2012; Siddik & Rahman 2014). All the data provider or data source of Gauge-Based precipitation productions have provided gridded precipitation datasets by interpolating the precipitation anomalies at the stations. As shown in Table 1, the gauge-based products of CRU, GPCC, PREC/L, UDEL, and CPC-Global already have the gridded precipitation data at the spatial resolution of 0.5° × 0.5°. Thereby, the Thiessen Polygons method can be used in this study to calculate the monthly areal mean rainfall of every river basin worldwide as follows:
formula
(1)
where Rb is the areal mean rainfall of a river basin (mm year−1), Ri is the rainfall in the Thiessen polygon i (mm year−1), Si is the area in the Thiessen polygon i (km2), and n is the number of Thiessen polygons within the river basin.
Table 1

Global precipitation datasets

NameSourceSpatial resolutionSpatial coverageTemporal resolutionTemporal coverageReferences
Gauge-based products 
CRU The CRU of the University of East Anglia 0.5° × 0.5° Global Monthly 1901–2020 Harris et al. (2014)  
GPCC GPCC 0.5° × 0.5° Global Monthly 1891–2016 Rudolf et al. (2010)  
PREC/L NOAA/ESRL/PSD 0.5° × 0.5° Global Monthly 1948–2020 Chen et al. (2002)  
UDEL NOAA/ESRL/PSD 0.5° × 0.5° Global Monthly 1900–2017 Willmott & Matsuura (2001)  
CPC-Global NCEP/Climate Prediction Center 0.5° × 0.5° Global Daily 1979–2020 Chen et al. (2008)  
Satellite-based products 
CMAP NOAA/ESRL/PSD 2.5° × 2.5° Global Monthly 1979–2020 Xie & Arkin (1997)  
GPCP NOAA/ESRL/PSD 2.5° × 2.5° Global Monthly 1979–2020 Adler et al. (2012, 2016
MSWEP CPC/GPCC/CMORPH 0.5° × 0.5° Global Daily/3 hourly 1979–2020 Beck et al. (2017)  
GPCP_PEN_v2.2 OPI, SSM/I, GPI, MSU 2.5° × 2.5° Global 5-daily 1979–2017 Xie et al. (2003)  
Reanalysis products 
NECP2 NOAA/ESRL/PSD 1.875° × 1.875° Global Monthly/6 hourly 1979–2020 Kalnay et al. (1996),Kanamitsu et al. (2002)  
ERA5 ECMWF 1.5° × 1.5°/0.75° × 0.75° Global Monthly /6 hourly 1979–2020 Dee et al. (2011)  
JRA-55 NOAA 60 km Global Monthly/3 hourly /6 hourly 1958–2020 Ebita et al. (2011)  
NameSourceSpatial resolutionSpatial coverageTemporal resolutionTemporal coverageReferences
Gauge-based products 
CRU The CRU of the University of East Anglia 0.5° × 0.5° Global Monthly 1901–2020 Harris et al. (2014)  
GPCC GPCC 0.5° × 0.5° Global Monthly 1891–2016 Rudolf et al. (2010)  
PREC/L NOAA/ESRL/PSD 0.5° × 0.5° Global Monthly 1948–2020 Chen et al. (2002)  
UDEL NOAA/ESRL/PSD 0.5° × 0.5° Global Monthly 1900–2017 Willmott & Matsuura (2001)  
CPC-Global NCEP/Climate Prediction Center 0.5° × 0.5° Global Daily 1979–2020 Chen et al. (2008)  
Satellite-based products 
CMAP NOAA/ESRL/PSD 2.5° × 2.5° Global Monthly 1979–2020 Xie & Arkin (1997)  
GPCP NOAA/ESRL/PSD 2.5° × 2.5° Global Monthly 1979–2020 Adler et al. (2012, 2016
MSWEP CPC/GPCC/CMORPH 0.5° × 0.5° Global Daily/3 hourly 1979–2020 Beck et al. (2017)  
GPCP_PEN_v2.2 OPI, SSM/I, GPI, MSU 2.5° × 2.5° Global 5-daily 1979–2017 Xie et al. (2003)  
Reanalysis products 
NECP2 NOAA/ESRL/PSD 1.875° × 1.875° Global Monthly/6 hourly 1979–2020 Kalnay et al. (1996),Kanamitsu et al. (2002)  
ERA5 ECMWF 1.5° × 1.5°/0.75° × 0.75° Global Monthly /6 hourly 1979–2020 Dee et al. (2011)  
JRA-55 NOAA 60 km Global Monthly/3 hourly /6 hourly 1958–2020 Ebita et al. (2011)  

Definition of the dry and wet seasons

To detect seasonal discrepancies in precipitation, the calendar year was divided into dry, normal, and wet seasons (see results in Supplementary material, Figures S2 and S3). The three successive months in which the sum of the multi-year average runoff was the lowest (highest) was considered to be the dry (wet) season (Chou & Lan 2012; Chou et al. 2013). The remaining six months were classified as the normal season. However, it should be noted that different river basins have different hydroclimatic characteristics with different duration of dry seasons. Thereby, our selection criteria of dry seasons might be improper for a small portion of river basins.

TFPM-MK method for trend analysis

The Mann–Kendall trend test method proposed by Mann (1945) and Kendall (1975) has been widely used to detect variation trends in hydro-meteorological time series data (Zareiee 2014). One assumption of this method is that the data series should be independent, and this is not true for many hydrological time series. Consequently, this Mann–Kendall trend test may overestimate the significance of both positive and negative trends (Yue & Wang 2002; Su et al. 2018). To overcome this, we applied the Mann–Kendall trend test with trend-free pre-whitening to evaluate precipitation trends.

For an assumed time series (x1, x2, x3, … xn-1, xn), the equations of the traditional Mann–Kendall trend test method are as follows (Equations (2)–(5)):
formula
(2)
formula
(3)
formula
(4)
formula
(5)
where n is the number of values in the time series; i and j represent years i and j, respectively; Var(s) is the variance; k is the number of groups of tied ranks, each with tj tied observations; and ZMK is the standardized test statistic. If ZMK> |Z1-α/2|, the null hypothesis is rejected and the time series shows an obvious upward trend. If ZMK< −|Z1-α/2|, the time series exhibits a significant downward trend.
The autocorrelation coefficient rm (m = 1) of the time series X is calculated by Equation (6), and the upper (Lu) and lower (Ld) limits of rm can be calculated by Equations (7) and (8), respectively:
formula
(6)
formula
(7)
formula
(8)

If LdrmLu, the time series are independent and the trends can be evaluated using the traditional Mann–Kendall trend test method. Otherwise, the time series need to be revised to meet the pre-whitening requirements. The processes are as follows:

The non-parametric Theil-Sen approach (TSA) slope (b) in the time series is calculated using Equation (9):
formula
(9)
Based on the non-parametric TSA slope (b) and the autocorrelation coefficient (rm), a new time series y that is free of the effects of autocorrelation can be reconstructed using Equations (10)–(12). The trends of this new time series can then be estimated using the traditional Mann–Kendall trend test method:
formula
(10)
formula
(11)
formula
(12)

MPD of the multi-year average annual precipitation

The MPD of the multi-year average annual precipitation is defined as follows:
formula
(13)
where max_P and min_P represent the maximum and the minimum multi-year average annual precipitation respectively, %.

Data sources

Table 1 lists 12 precipitation data products used in this study, which represent the most widely used rainfall data sources. These products include five gauge, four satellite, and three reanalysis-based products. The common product data range is 1979–2016; hence, this period was selected as the research period in this study.

Discrepancies in annual precipitation

Figure 1 shows that the central estimate of global annual precipitation during 1979–2016 was 817.1 mm year−1 (694.8 mm year−1 in CPC-Global to 986.1 mm year−1 in NECP2, Supplementary material, Table S1), which is nearly consistent with previous findings (Sun et al. 2018). At the river basin scale, each dataset type has unique characteristics. For instance, gauge-based products (Figure 1(a)–(e)) have the highest number (3,092, 46.6% of the global land surface) of dry river basins with annual precipitation less than 500 mm year−1, while satellite- and reanalysis- based products have 2,654 (40.4% of the global land surface) and 2,343 (26.3% of the global land surface) dry river basins, respectively. Conversely, reanalysis-based products have the largest number of wet river basins (34.1% of total numbers) with annual precipitation more than 1,000 mm year−1, while the gauge-based products have the smallest number (1904). This large difference in estimating annual precipitation across the precipitation products at river basin scale is caused by the difference in data sources, quality control schemes, and estimation procedures. Apparently, reanalysis-based products, especially the NECP2 dataset (Figure 1(k)), have higher precipitation in global river basins, while gauge-based products have the lowest. Overestimation by reanalysis data is primarily located in the Southern Hemisphere, including the desert regions of Sahara, Australia, and Arabian Peninsula (Figure 1(k)–(m)). Satellite-based products (Figure 1(f)–(i)) also have higher precipitation compared to gauge-based products, especially in Europe and North Asia. Importantly, the differences among precipitation estimates from gauge-based, reanalysis, and satellite-gauge merged products are mainly distributed in the river basins of northern Africa, northern North America and Greenland. These regions have sparse measurements owing to sparse populations and complex terrain. Furthermore, high-elevation regions have relatively warm clouds, and incorrect discrimination between rain clouds and non-rain clouds with thermal IR could cause the IR rainfall retrieval algorithms to miss light-precipitation events and underestimate total rainfall (Sun et al. 2018). These factors might be the causes for the large difference across the precipitation datasets.
Figure 1

Multi-year average annual mean precipitation (mm year−1) in the 6,292 river basins in 1979–2016 based on the 12 precipitation productions. The river basins with red color represent the low annual mean precipitation, while the river basins with blue color show the high annual mean precipitation. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis products.

Figure 1

Multi-year average annual mean precipitation (mm year−1) in the 6,292 river basins in 1979–2016 based on the 12 precipitation productions. The river basins with red color represent the low annual mean precipitation, while the river basins with blue color show the high annual mean precipitation. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis products.

Close modal
Figure 2 shows the maximum difference in annual precipitation between data products for each river basin. A total of 3,125 river basins with an area of 5,989.1 × 104 km2 (44.2% of global land area) have a maximum annual precipitation difference of over 500 mm year−1, mainly in the river basins of Greenland, Africa, Oceania, and South Asia (desert regions with few rain gauges, see Supplementary material, Figure S1 and Figure 2(a)). Conversely, only 883 river basins (1,075.7 × 104 km2) have a maximum difference of less than 200 mm year−1. These are generally located in basins with high-density rain gauges, including North America and the Lena River Basin in Asia. This indicates that when we need to estimate the precipitation in these river basins, it might be more reliable and accurate to choose the gauge-based precipitation products.
Figure 2

Difference in the multi-year average annual mean precipitation in 1979–2016 between the 12 precipitation productions. (a) The maximum difference in the multi-year average annual precipitation between the 12 precipitation productions, mm year−1; (b) presents the MPD of the multi-year average annual precipitation between the 12 precipitation productions. Please see the calculation in Section 2.1.4. The river basins with red color represent the low difference in estimating annual mean precipitation across the datasets, while the river basins with blue color show the high difference in estimating annual mean precipitation across the datasets.

Figure 2

Difference in the multi-year average annual mean precipitation in 1979–2016 between the 12 precipitation productions. (a) The maximum difference in the multi-year average annual precipitation between the 12 precipitation productions, mm year−1; (b) presents the MPD of the multi-year average annual precipitation between the 12 precipitation productions. Please see the calculation in Section 2.1.4. The river basins with red color represent the low difference in estimating annual mean precipitation across the datasets, while the river basins with blue color show the high difference in estimating annual mean precipitation across the datasets.

Close modal

Figure 2(b) shows the percentage difference between the maximum and minimum annual precipitation for each river basin. This ratio exceeds 100% for more than half of the world's river basins (3,708 covering an area of 6,198.4 × 104 km2), mainly in Greenland, Africa, western Asia, and Oceania. Only 177 river basins (939.2 × 104 km2) had an MPD smaller than 30%, and these were concentrated in South America and Europe. The degree of variability in annual precipitation between the different data products questions the findings of previous studies that have used and applied these precipitation data. Indeed, the level uncertainty may be substantial when the percentage differences in annual precipitation between the datasets are considered.

The world's 15 largest river basins occupy 29.3% of the global land area (3,977.1 × 104 km2), support large populations, and are among the most diverse land-based ecosystems (Best 2019). We found that annual precipitation in the 13 largest river basins varied by over 200 mm year−1. Variability was especially high in the Nile (697.0 mm year−1), Tamanrasett (696.9 mm year−1), Ganges (679.6 mm year−1), and Lake Chad River Basins (699.8 mm year−1). The NECP precipitation product significantly overestimated annual precipitation in the Nile, Zaire, Parana, Niger, Tamanrasett, and Lake Chad River Basins.

An understanding of annual precipitation trends is important for analyzing the varied hydrological processes, their effects on vegetation dynamics, and the prediction of future climate change (Chu et al. 2019). Figure 3 shows trends in annual precipitation in 1979–2016. The precipitation datasets display considerable differences in terms of annual trends at the basin scale. For instance, the NECP2 precipitation dataset indicates that 4,310 river basins have a significant increasing trend in annual precipitation (at a confidence interval of 90%), including the Amazon, Nile, and Mississippi River Basins. Conversely, the GPCP_PEN_v2.2 precipitation product shows that only 821 catchments display a significant increasing trend, a five-fold difference compared with NECP2. However, annual precipitation in many river basins showed a downward trend using the ERA5 product, especially in the southern South America, most of Central Africa, and the East Asia. Furthermore, 1,589 river basins show a significant decreasing trend in annual precipitation using the UDEL product, while only 139 river basins display a similar trend using MSWEP data. Interestingly, no river basins displayed a consistent annual precipitation trend (either increasing or decreasing) across all the products investigated. For instance, annual precipitation trends in the Yangtze River Basin were significantly increasing in the PRECL and CPC-Global products, while CRU, NECP2, ERA5, and JRA-55 products all indicated a significant decrease. The remaining six products (i.e., GPCC, UDEL, CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2) suggested that precipitation in the Yangtze River Basin remained nearly consistent. GPCC is used as the reference object in this study because it is the largest gauge-observation dataset, with data from more than 70,000 different stations. This reference object can help us to evaluate the quality of the different precipitation products. Comparing with the GPCC dataset, we find that PREC/L has a more reliable estimation of the precipitation trends in the river basins of South America, Australian, Europe and the Eastern Asia. The precipitation trends' estimations in Africa are more reliable in the UDEL precipitation product, due to its similar estimations with the GPCC precipitation products.
Figure 3

Variations trends in the multi-year average annual mean precipitation based on the TFPM-MK Method. Note: |ZMK| > 2.32, |ZMK| > 1.96, and |ZMK| > 1.64 mean the annual precipitation shows an increasing or decreasing trend with the confidence level over 99, 95, and 90%, respectively; |ZMK| < 1.64 means the annual precipitation had no trend. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis products.

Figure 3

Variations trends in the multi-year average annual mean precipitation based on the TFPM-MK Method. Note: |ZMK| > 2.32, |ZMK| > 1.96, and |ZMK| > 1.64 mean the annual precipitation shows an increasing or decreasing trend with the confidence level over 99, 95, and 90%, respectively; |ZMK| < 1.64 means the annual precipitation had no trend. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis products.

Close modal

Dry season precipitation changes and uncertainty

Precipitation variation during the dry season negatively impacts agriculture, ecosystem stability, and navigation, and managing these impacts requires accurate precipitation data (Bartzke et al. 2018). Globally, average dry season precipitation is 121.8 mm year−1; however, this varies from 94.1 (CPC-Global) to 203.7 mm year−1 (ERA5; Figure 4). Reanalysis precipitation products had the highest dry season precipitation with an average of 163.4 mm year−1 (Figure 4(j)–(l)), followed by satellite (116.7 mm year−1; Figure 4(f)–(i)) and gauge-based products (100.8 mm year−1; Figure 4(a)–(e)). Gauge-based products tended to be the most consistent.
Figure 4

Multi-year average of the precipitation of the dry season in the 6,292 river basins in 1979–2016 between the 12 precipitation productions. (a–l) Multi-year average precipitation of the dry season in 1979–2016, mm. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis products.

Figure 4

Multi-year average of the precipitation of the dry season in the 6,292 river basins in 1979–2016 between the 12 precipitation productions. (a–l) Multi-year average precipitation of the dry season in 1979–2016, mm. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis products.

Close modal

The driest river basins were mainly in Africa, Oceania, and Central Asia. These regions have a high risk of drought, with dry season precipitation averaging less than 20 mm year−1. Conversely, Europe, eastern North America, and South America have abundant water resources, with dry season precipitation exceeding 100 mm year−1. Both satellite (Figure 4(f)–(i)) and reanalysis (Figure 4(j)–(l)) based products display a major variability. For instance, GPCP and GPCP_PEN_v2.2 (satellite precipitation products) indicate higher dry season precipitation in Europe and eastern North America compared to other satellite-based products (CMAP and MSWEP). Of the reanalysis-based products, NECP2 tends to overestimate dry season precipitation in the Southern Hemisphere, especially in Africa and Oceania. It should be noted that all the reanalysis precipitation products deviate notably from the gauge-based products and satellite-based products, especially in mountainous and coastal regions. This might be caused by the reanalysis models' inability to represent the effects of complex orography and/or sparse observational inputs for assimilations (Kim & Park 2016).

Figure 5 displays variation trends in dry season precipitation. Similar to annual precipitation trends, we found a major variability between these data products. MSWEP had the lowest number of river basins with a significant decreasing trend in dry season precipitation (348 river basins with an area of 560.1 × 104 km2), which was half that of UDEL (1,375 river basins with 1,662.1 × 104 km2). GPCP_PEN_v2.2 had the lowest number of river basins with a significant increasing trend (600 river basins with an area of 1,707.7 × 104 km2), which was approximately one-third that of CRU (2,039 river basins with 6,157.2 × 104 km2). No river basins showed a consistent trend in 12 precipitation products evaluated. In terms of the precipitation variation trend in river basins, there even exists an opposite trend in the same basin when different products are used. For instance, most river basins in Africa display a significant decreasing trend using ERA5 and JRA-55 products, especially for ERA5. In contrast, the dry season precipitation of river basins in Africa shows an increasing trend or no trend when CRU and NECP2 products are used.
Figure 5

Variation trends in the multi-year average of the precipitation of the dry season in 1979–2016 based on the 12 precipitation productions. (a–l) Variation trends in the average precipitation of the dry season based on the TFPM-MK Method. Note: |ZMK| > 2.32, |ZMK| > 1.96, and |ZMK| > 1.64 mean the precipitation in the dry season shows an increasing or decreasing trend with the confidence level over 99, 95, and 90%, respectively; |ZMK| < 1.64 means the precipitation had no trend. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis-based products.

Figure 5

Variation trends in the multi-year average of the precipitation of the dry season in 1979–2016 based on the 12 precipitation productions. (a–l) Variation trends in the average precipitation of the dry season based on the TFPM-MK Method. Note: |ZMK| > 2.32, |ZMK| > 1.96, and |ZMK| > 1.64 mean the precipitation in the dry season shows an increasing or decreasing trend with the confidence level over 99, 95, and 90%, respectively; |ZMK| < 1.64 means the precipitation had no trend. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis-based products.

Close modal
Figure 6(a) shows that approximately half of all global river basins (4,565 river basins with an area of 9,606.4 × 104 km2) had a maximum discrepancy in dry season rainfall of >100 mm year−1, mainly in the Southern Hemisphere. Conversely, only 760 river basins (991.4 × 104 km2) had a maximum discrepancy < 40 mm year−1. These basins were primarily located in the Northern Hemisphere, especially in northern North America and East Asia. Figure 6(b) shows that the MPD in the average dry season precipitation was higher than 100% in most river basins. Discrepancies were the most pronounced in Africa, Oceania, southwest Asia, and Greenland, where the MPD was higher than 500% in most river basins. The uncertainty in these precipitation products indicates that providing prompt and accurate guidelines for combating droughts, especially in Africa, remains very challenging.
Figure 6

Difference in the multi-year average of the precipitation of the dry season during 1979–2016 between the 12 precipitation productions. (a) Maximum difference in the multi-year average precipitation of the dry season between the 12 precipitation productions, mm year−1; (b) the MPD of the multi-year average precipitation of the dry season between the 12 precipitation productions. Please see the calculation in Section 2.1.4. The river basins with red color represent the low difference in estimating the multi-year average of the precipitation of the dry season across the datasets, while the river basins with blue color show the high difference in estimating the multi-year average of the precipitation of the dry season across the datasets.

Figure 6

Difference in the multi-year average of the precipitation of the dry season during 1979–2016 between the 12 precipitation productions. (a) Maximum difference in the multi-year average precipitation of the dry season between the 12 precipitation productions, mm year−1; (b) the MPD of the multi-year average precipitation of the dry season between the 12 precipitation productions. Please see the calculation in Section 2.1.4. The river basins with red color represent the low difference in estimating the multi-year average of the precipitation of the dry season across the datasets, while the river basins with blue color show the high difference in estimating the multi-year average of the precipitation of the dry season across the datasets.

Close modal

Wet season precipitation changes and uncertainty

To project future flood risk, it is necessary for climate models to accurately represent the basic features of the historical climate system (Hall et al. 2019), which relies on accurate wet season precipitation data. Figure 7 shows that the global average wet season precipitation is 349.8 mm year−1 among the 12 precipitation products, ranging from 307.3 mm year−1 (CPC-Global) to 401.3 mm year−1 (MSWEP). At the river basin scale, an average of 2,926 river basins among the 12 precipitation products, ranging from 2,580 (CPC-Global) to 3,219 (MSWEP), had average wet season precipitation of > 250 mm year−1. These basins are primarily in South America, northern Africa, northern Oceania, and southern Asia. Gauge- and satellite-based products display relatively consistent wet season precipitation distribution, with high (or low) rainfall intensity in the Southern (or Northern) Hemisphere. Conversely, reanalysis products display extensive variation in terms of wet season precipitation distribution. For instance, NECP2 overestimates wet season precipitation in Oceania and North Africa and underestimates rainfall in the Amazon River basin and East Asia. In terms of wet season precipitation in different river basins, the river basins in North Africa have the highest rainfall (>700 mm), and precipitation with 400–700 mm in other basins are mainly distributed in the northern South America, southern East Asia, and Central Africa. In contrast, the North and South polar river basins receive very little rainfall, as do southern Africa and Central Asia.
Figure 7

Multi-year average of the precipitation of the wet season in the 6,292 river basins in 1979–2016 based on the 12 precipitation productions. (a–l) Multi-year average precipitation of the wet season in 1979–2016, mm. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis-based products.

Figure 7

Multi-year average of the precipitation of the wet season in the 6,292 river basins in 1979–2016 based on the 12 precipitation productions. (a–l) Multi-year average precipitation of the wet season in 1979–2016, mm. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis-based products.

Close modal
Figure 8 highlights the tremendous uncertainty in wet season precipitation trends. For instance, CRU with 555 river basins (864.8 × 104 km2) showed a significant increasing trend, less than one-sixth that of NECP (3,756 river basins with an area of 7,334.4 × 104 km2). Based on the trends in NECP, more than half of the global land area is facing an increased flood risk rising due to increased wet season precipitation. Furthermore, CRU had the maximum number of river basins with a significant decreasing trend in wet season precipitation (1,586 river basins with an area of 3,877.3 × 104 km2), which is more than four times that of MSWEP (231 river basins with an area of 218.5 × 104 km2). No river basins displayed a consistent trend across 12 precipitation products evaluated. High mountain regions, including Himalayas, has the largest difference in estimating the variation trend in multi-year average precipitation of wet season between gauge-based products and satellite-based precipitation products. The causes might be as follow: in these regions, gauge-based products lack of rainfall stations or gauges, which may deviate this kind of precipitation products from the real value of the trend of the precipitation in wet season. Besides, the high mountain regions have the varied cloud cover, which can severely disturb the satellite-based precipitation products to capture a more accurate estimate of precipitation trend.
Figure 8

Variation trend in multi-year average precipitation of the wet season in 1979–2016 based on the 12 precipitation productions. (a–l) Variation trends in the average precipitation of the wet season based on the TFPM-MK Method. Note: |ZMK| > 2.32, |ZMK| > 1.96, and |ZMK| > 1.64 mean the precipitation in the wet season shows an increasing or decreasing trend with the confidence level over 99, 95, and 90%, respectively; |ZMK| < 1.64 means the precipitation had no trend. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis-based products.

Figure 8

Variation trend in multi-year average precipitation of the wet season in 1979–2016 based on the 12 precipitation productions. (a–l) Variation trends in the average precipitation of the wet season based on the TFPM-MK Method. Note: |ZMK| > 2.32, |ZMK| > 1.96, and |ZMK| > 1.64 mean the precipitation in the wet season shows an increasing or decreasing trend with the confidence level over 99, 95, and 90%, respectively; |ZMK| < 1.64 means the precipitation had no trend. CRU, GPCC, PREC/L, UDEL, and CPC-Global are the gauge-based products. CMAP, GPCP, MSWEP, and GPCP_PEN_v2.2 are the satellite-based products. NECP2, ERA5, and JRA-55 are the reanalysis-based products.

Close modal
Figure 9(a) shows the degree of variability in wet season precipitation between data products. Results show that average wet season precipitation varied by > 200 mm year−1 in 4,123 river basins with an area of 10,257.7 × 104 km2. These basins were mainly in northern South America, northern Africa, southern Asia, and Oceania. Of these river basins, 1,715 river basins (3,079.9 × 104 km2) had variations exceeding 500 mm year−1. Figure 9(b) displays the MPD between the highest and lowest data products for each river basin. Differences greater than 300% were found in 1,411 river basins (1,756.9 × 104 km2), primarily in North Africa. This uncertainty is likely to negatively impact accurate flood prediction and successful flood mitigation strategies.
Figure 9

Difference in the multi-year average precipitation of the wet season in 1979–2016 between the 12 precipitation productions. (a) Maximum difference in the multi-year average precipitation of wet season between the 12 precipitation productions, mm year−1; (b) MPD of the multi-year average precipitation of wet season between the 12 precipitation productions. Please see the calculation given in Section 2.1.4. The river basins with red color represent the low difference in estimating the multi-year average precipitation of the wet season across the datasets, while the river basins with blue color show the high difference in estimating the multi-year average precipitation of the wet season across the datasets.

Figure 9

Difference in the multi-year average precipitation of the wet season in 1979–2016 between the 12 precipitation productions. (a) Maximum difference in the multi-year average precipitation of wet season between the 12 precipitation productions, mm year−1; (b) MPD of the multi-year average precipitation of wet season between the 12 precipitation productions. Please see the calculation given in Section 2.1.4. The river basins with red color represent the low difference in estimating the multi-year average precipitation of the wet season across the datasets, while the river basins with blue color show the high difference in estimating the multi-year average precipitation of the wet season across the datasets.

Close modal

Most previous studies evaluate the performance of precipitation products at national, continental, and global scales. However, there is a lack of a comprehensive evaluation of precipitation products at catchment scale. Our study may bring some important implications on the applications of precipitation products on water resources management, flood control and disaster reduction and reservoir operations. Consistent with the previous studies, we all concluded that the NECP precipitation dataset may largely overestimate the global land precipitation products.

An accurate representation of the historical and current climate is necessary to provide confidence in future climate projections. As a fundamental driver of the global hydrological cycle, accurate precipitation data are essential. In this study, we provided a comprehensive comparison of 12 precipitation products that have been used extensively as abundant data sources of global precipitation patterns, to quantify the uncertainty in these data at the river basin scale. Our results revealed extensive discrepancies between precipitation data products. In particular, reanalysis-based precipitation products, especially NECP, had the highest uncertainty compared to other data sources. Specifically, we found that NECP tended to overestimate dry season precipitation in the Southern Hemisphere, which is consistent with the findings of similar studies. During the wet season, NECP precipitation at the global scale was fairly accurate. While data discrepancies were large in both the wet and dry seasons, dry season rainfall distribution revealed the greatest disparity between data sources.

These very large discrepancies in precipitation estimates at the basin scale raise concerns with respect to the findings of many studies that have used these data for regional hydrological, agricultural, and climate analysis. This is particularly problematic in data-poor regions of the Southern Hemisphere, such as northern Africa, where accurate information with respect to dry season rainfall trends is needed to reduce the impact of drought. With the development and progress of future monitoring methods, the diversity and accuracy of observation methods, and the increasing number of observation sites, we are more confident to select a few better rainfall analysis products. For instance, in the future, we can do some rainfall experiments in the small watershed scale to judge which rainfall product is better based on the experimental results.

GJW acknowledges the National key research and development program [grant number: 2022YFC3005404], the Youth Project of National Natural Science Foundation China [grant number: 42301018], the Independent Innovation Project of Changjiang Design Group Co., Ltd (Grant CX2020Z19) and China Postdoctoral Science Foundation funded project (2022M710490). We acknowledge Yuanfang Chai who provided the valuable writing and comments.

X.H. and X.Y. led the writing, designed the research and performed the data analysis. H.X., Q.G., Z.Z., and X.C. provided valuable comments.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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