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Article

Assessment of ERA5-Land Reanalysis Precipitation Data in the Qilian Mountains of China

by
Lihui Qian
and
Peng Zhao
*
School of Computer Science, Huainan Normal University, Huainan 232038, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(7), 826; https://doi.org/10.3390/atmos16070826
Submission received: 14 May 2025 / Revised: 30 June 2025 / Accepted: 3 July 2025 / Published: 7 July 2025
(This article belongs to the Section Meteorology)
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Abstract

Precipitation serves as a crucial indicator of climate change and a vital part of the water cycle in mountainous regions. ERA5-Land, a cutting-edge global reanalysis dataset designed for land applications, has been extensively utilized in climate-related studies. In this research, we assessed the reliability of ERA5-Land monthly averaged reanalysis precipitation data in the Qilian Mountains (QLM). We did this by comparing it with the observations from 17 meteorological stations spanning from 1979 to 2017. The findings indicated that, overall, the ERA5-Land reanalysis precipitation data tended to overestimate the observed precipitation in the Qilian Mountains. The determination coefficient (R2) of the linear regression between ERA5-Land reanalysis precipitation and the observations was 0.97. This value implies that ERA5-Land reanalysis precipitation generally has good applicability in the Qilian Mountains. However, the annual-scale root mean square error (RMSE) was 3.97. This suggests that ERA5-Land reanalysis precipitation data cannot be directly applied to studies at a single meteorological station. The deviation between the ERA5-Land reanalysis precipitation data and the observed precipitation data can be ascribed to the altitude difference between meteorological stations and ERA5-Land grid points. Generally, as the altitude difference between meteorological stations and ERA5-Land grid points increases, the precipitation deviation also rises. This research can furnish a reference for the application of ERA5-Land reanalysis precipitation data in the Qilian Mountains.

1. Introduction

Mountainous regions like the Qilian Mountains and Tianshan play a vital role as ecological protection barriers and water resource suppliers in northwest China [1,2,3,4]. The Qilian Mountains, with their distinctive climatic conditions and intricate geographical environment, are among the most ecologically vulnerable and climate-sensitive areas [5,6,7]. In the context of global climate change, precipitation patterns in the Qilian Mountains have undergone remarkable alterations [6,8,9,10]. Precipitation is a key indicator for studying climate change in this region, and its variations significantly impact factors such as soil moisture, snow cover, and runoff [1,11,12].
The Qilian Mountains exhibit a typical mountainous precipitation climate, with annual totals ranging from 200 to 700 mm, primarily influenced by altitude and orographic lifting. Precipitation is concentrated in June–September, accounting for ~70% of the annual amount, while winter precipitation is scarce and highly variable. The complex topography, including high-altitude plateaus and deep valleys, creates heterogeneous precipitation patterns that challenge the accuracy of reanalysis datasets. Past research on precipitation changes in the Qilian Mountains predominantly relied on observed precipitation data [13,14]. Nevertheless, due to the complex terrain within the Qilian Mountains, there is a scarcity of meteorological stations at high altitudes, particularly above 3500 m. This limited distribution of stations restricts the comprehensiveness of the observed precipitation data, making it challenging to analyze the detailed characteristics of precipitation changes. As a result, there is an urgent need to employ reanalysis precipitation data, which feature high spatial resolution, long-term time series, and high accuracy [8,15]. Although reanalysis data exhibit relatively low errors on a global scale, their applicability requires rigorous assessment when applied to small-scale regional climate research [16,17,18,19,20].
Previous studies on the applicability of reanalysis data in the Qilian Mountains mainly centered on two aspects. One aspect was the assessment of the applicability of a single set of reanalysis data [21,22], and the other was the comparative analysis of the applicability of multiple sets of reanalysis data [8,15]. For instance, Zhao et al. [21] assessed the suitability of the ERA-Interim reanalysis air temperature data within the context of the Qilian Mountains. Their findings indicated that while the dataset demonstrated general reliability, notable discrepancies persisted between the reanalysis-derived temperatures and in situ observational measurements. Zhao et al. [23] investigated the applicability of ERA5-Land reanalysis air temperature data in the Qilian Mountains and determined that it could accurately reflect the air temperature variations, thereby providing a valuable reference for analyzing such changes. Huai et al. [15] analyzed the applicability of four reanalysis datasets (ERA5, ERA-Interim, HAR, and JRA-55) in the Qilian Mountains and concluded that ERA5 was more suitable than the other three datasets. Ren et al. [10] evaluated the applicability of MERRA2 reanalysis precipitation data in the Qilian Mountain area and found that its average bias was −0.69 mm, suggesting an underestimation of the observed precipitation.
The ERA5-Land of the European Centre for Medium-Range Weather Forecasts (ECMWF) represents a leading-edge global reanalysis dataset for land-related applications [24,25,26]. Compared with ERA5 and ERA-Interim reanalysis data, ERA5-Land reanalysis data offer a higher resolution [27,28,29]. Gomis-Cebolla et al. [17] evaluated the ERA5 and ERA5-Land reanalysis precipitation datasets over Spain and found that ERA5-Land/ERA5 showed a good capacity to reproduce the spatial patterns and temporal trends of the observations. Previous studies have evaluated the ERA5-Land reanalysis data. Mihalevich et al. [30] evaluated and corrected the ERA5-Land reanalysis data in the Colorado River basin, and the results showed that elevation corrections improved air temperature in the basin. Guo et al. [31] evaluated the ERA5 precipitation in the Yellow River basin and found that RA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation in the Yellow River basin.
ERA5-Land offers higher spatial resolution (0.1° × 0.1°) and land-specific processing compared to ERA5, ERA-Interim, HAR, and JRA-55. This makes it better suited for capturing fine-scale topographic effects on precipitation in mountainous regions like the Qilian Mountains. However, there has been a lack of research on the applicability of ERA5-Land reanalysis precipitation data in the Qilian Mountains. Consequently, this study utilized the observed precipitation data from 17 meteorological stations in the Qilian Mountains between 1979 and 2017. By applying error analysis indices such as bias, correlation analysis (r), and root mean square error (RMSE), we evaluated the differences between the ERA5-Land multi-year mean of the monthly sum precipitation and the observed precipitation data. The findings of this study aim to offer useful insights for the application of ERA5-Land reanalysis precipitation data in the Qilian Mountains.

2. Data and Methods

ERA5-Land precipitation data (1950–present) were obtained from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS; DOI: 10.24381/cds.68d2bb30), accessed on 13 June 2025 [32]. The data spanned from 1979 to 2017 and had a spatial resolution of 0.1° × 0.1° [32]. The spatial scope covered the area from 35.8 to 40° N latitude and 93.5 to 104° E longitude, fully encompassing the entire Qilian Mountains, as depicted in Figure 1. The observed precipitation data (Po) of 17 meteorological stations in the Qilian Mountains during 1979–2017 were acquired from the National Data Center for Meteorological Sciences (https://data.cma.cn/, accessed on 1 July 2025). Information on latitude, longitude, and altitude of the 17 meteorological stations and local topography can refer to the reference [23]. The data providers had strictly inspected the quality of these observed precipitation data, ensuring their reliability in evaluating the applicability of reanalysis precipitation data. For comprehensive details about the 17 meteorological stations, refer to reference [21]. Using the latitude and longitude coordinates of the 17 meteorological stations, the ERA5-Land grid points nearest to each station were determined. To evaluate the discrepancies between Po and Pe, three metrics were applied: bias, correlation coefficient (r), and root mean square error (RMSE). Bias (mm): It measures the average difference between reanalysis and observed precipitation, indicating whether the reanalysis data systematically overestimates or underestimates actual rainfall. Pearson Correlation Coefficient (r): This index ranges from −1 to 1 and reflects the strength of the linear relationship between the reanalysis and observed data. A value close to 1 suggests strong correspondence in precipitation patterns. Root Mean Square Error (RMSE, mm): This metric quantifies the overall magnitude of deviations between reanalysis and observations, combining both systematic and random errors to evaluate the data’s accuracy. This approach circumvented the errors associated with spatial interpolation. Additionally, the error sources of the ERA5-Land reanalysis precipitation data were explored using the linear regression method.

3. Results

Figure 2 shows the comparison of monthly mean precipitation at all 17 meteorological stations in the Qilian Mountains with ERA5-Land reanalysis precipitation in 12 months. We can learn that the ERA5-Land precipitation data at all 17 meteorological stations tend to overestimate the observed precipitation at all 17 meteorological stations in the Qilian Mountains. When looking at different months, both the ERA5-Land reanalysis precipitation and the observed precipitation are largest in June to September (Figure 2). The determination coefficient between the ERA5-Land reanalysis precipitation and the observed precipitation is 0.97, suggesting that, generally speaking, the ERA5-Land precipitation data are quite applicable in the Qilian Mountains (Figure 3).
Table 1 presents a comparison between the monthly averaged precipitation at each of the 17 meteorological stations in the Qilian Mountains and the corresponding ERA5-Land precipitation. The correlation coefficients (r) of all stations exceed 0.75, with an average value of 0.861 across all stations. This suggests that the ERA5-Land precipitation data is capable of effectively reflecting the observed variations in precipitation within the Qilian Mountains. The average bias across all meteorological stations is 3.448 mm, and the largest bias, 10 mm, is observed at Station No. 7, which may be caused by the larger elevation difference between the ERA5-Land grid point and the meteorological station at No. 7. The mean root-mean-square error (RMSE) for all meteorological stations is 5.945 mm.
Based on the seasonal-scale comparison of ERA5-Land reanalysis precipitation and observed precipitation (Table 2), the precipitation bias at Station No. 14 is negative in all four seasons. The mean biases for spring, summer, autumn, and winter amount to 3.15 mm, 5.86 mm, 3.93 mm, and 1.35 mm, respectively. The precipitation bias is smallest in winter, with six meteorological stations having a positive bias of less than 1 mm and 11 stations having a positive bias of less than 2 mm. The mean correlation coefficients for spring, summer, autumn, and winter are 0.663, 0.611, 0.725, and 0.481, respectively. This shows that the ERA5-Land reanalysis precipitation data perform best in capturing autumn precipitation changes in the Qilian Mountains. The average RMSE values for spring, summer, autumn, and winter are 3.84, 7.61, 4.59, and 1.48, respectively. Since the RMSE is largest in summer, it is necessary to use ERA5-Land reanalysis precipitation data with caution when analyzing summer precipitation in the Qilian Mountains.
The notable difference in precipitation bias between the two stations (e.g., Station 7 with +10.00 mm and Station 14 with −1.28 mm in Table 3) primarily stems from their altitude disparities relative to ERA5-Land grid points and topographic influences. Station 7 likely resides at a lower altitude than its corresponding grid point, leading ERA5-Land to overestimate precipitation due to the model’s tendency to represent lower-altitude climatic conditions more accurately. In contrast, Station 14, situated at a higher altitude, experiences underestimated precipitation because the reanalysis data struggles to capture enhanced orographic precipitation effects at elevated sites. This aligns with our finding that precipitation bias correlates with elevation differences (R2 = 0.2037 for annual scale), where higher-altitude stations like 14 exhibit negative biases and lower-altitude stations like 7 show positive biases. Additionally, the complex topography of the Qilian Mountains introduces microclimatic variations not fully resolved by ERA5-Land’s 0.1° × 0.1° resolution. Station 7 might be in a valley or sheltered area where the model overemphasizes precipitation, while Station 14′s exposed high-altitude location could enhance local precipitation (e.g., via orographic lifting) that the reanalysis underrepresents. These factors highlight the need for site-specific bias correction when applying ERA5-Land to individual stations in mountainous regions.
Regarding the annual-scale comparison (Table 3), seven meteorological stations have a positive bias of less than 2 mm, and only Station No. 14 has a negative precipitation bias (−1.28 mm), indicating that the ERA5-Land reanalysis precipitation data for Station No. 14 underestimate the observed precipitation. Station No. 7 has the largest precipitation bias, at 10 mm. The negative precipitation bias at Station No. 14 across all seasons can be primarily attributed to the altitude difference between the station and the corresponding ERA5-Land grid point. As discussed in the study, the precipitation deviation tends to increase with the elevation difference between meteorological stations and ERA5-Land grid points. Specifically, Station No. 14 likely has a higher altitude than the ERA5-Land grid point, leading the reanalysis data to underestimate the observed precipitation. This aligns with the annual-scale analysis, where Station No. 14 showed a negative bias of −1.28 mm, further indicating that the reanalysis model may underrepresent precipitation in higher-altitude regions. The topographic complexity of the Qilian Mountains, combined with the spatial resolution of ERA5-Land (0.1° × 0.1°), might also contribute to this discrepancy, as the model struggles to capture fine-scale topographic effects on precipitation. Additional factors, such as local climatic conditions or microtopographic influences, could exacerbate the bias, but the altitude difference is the primary driver based on our regression analysis (R2 = 0.2037 for annual bias vs. elevation difference). This highlights the need for bias correction or downscaling methods when applying ERA5-Land data to single stations in mountainous areas. The mean correlation coefficient across all meteorological stations is 0.627, suggesting that ERA5-Land reanalysis precipitation data can generally capture the variations in annual average precipitation within the Qilian Mountains with reasonable accuracy. The average RMSE of all meteorological stations is 3.97. The overall mean RMSE of all meteorological stations is 3.97, which suggests that caution should be exercised when applying ERA5-Land precipitation data to studies of individual meteorological stations.

4. Discussion

Figure 2 and Figure 3 illustrate that the ERA5-Land reanalysis precipitation data overestimate the observed precipitation data in the Qilian Mountains. Huai et al. [15] discovered that ERA5, JRA-55, and HAR reanalysis precipitation data also overestimated the observed precipitation in the Qilian Mountains, which aligns with the findings of this paper. Previous research has also indicated that reanalysis precipitation data overestimated the actual precipitation in other research regions [17,33]. As demonstrated in Table 3, the ERA5-Land reanalysis precipitation data exhibit the highest correlation coefficient with observed precipitation in autumn (r = 0.725) and the lowest in winter (r = 0.481), which aligns with the findings of previous research [8]. Table 3 shows that the average correlation coefficient of 17 meteorological stations on an annual scale is 0.627. Huai et al. [15] found that the correlation coefficient between ERA5, JRA-55, and HAR reanalysis precipitation data and the observed precipitation data in the Qilian Mountains was less than 0.6. The correlation coefficient between ERA5-Land reanalysis precipitation data and observed precipitation in the Qilian Mountains demonstrates a marginal increase compared to the findings reported in Reference [15].
Huai et al. [15] further revealed that the suitability of the ERA5 reanalysis precipitation data in the Qilian Mountains is inferior to that of temperature, wind, and pressure datasets. Figure 4 illustrates the linear correlation between the annual mean precipitation bias and the altitude difference between the observed precipitation (Po) and ERA5-Land reanalysis precipitation (Pe). The precipitation bias at the annual scale between Po and Pe might be attributed to the altitude difference between them (R2 = 0.2037). Generally, the greater the altitude difference between Po and Pe, the larger the precipitation bias. Figure 5 shows that the R2 values between the bias of Po and Pe and the altitude difference in spring, summer, autumn, and winter are 0.0863, 0.2821, 0.139, and 0.2457, respectively. This suggests that the altitude difference between meteorological stations and ERA5-Land grid points may induce precipitation bias at both the annual and seasonal scales. Thus, the implementation of bias correction and downscaling methodologies is pivotal to mitigating the bias in ERA5-Land reanalysis precipitation data and augmenting its reliability [34,35,36]. For example, Jiang et al. [33] utilized the convolution neural network (CNN) model to conduct a downscaling analysis of the ERA5 reanalysis precipitation data on the Qinghai-Tibet Plateau (QTP). The results demonstrated that the downscaled ERA5 reanalysis precipitation data had higher accuracy than the original data. Besides the altitude difference factor, the bias of reanalysis precipitation data may be associated with other elements. Jiao et al. [37] found that the accuracy of reanalysis precipitation products is closely linked to topographic conditions and climate types. Hu et al. [38] suggested that reanalysis precipitation data should be used cautiously in research on complex topographic areas. Lei et al. [39] discovered that reanalysis precipitation data had difficulty simulating precipitation changes in complex topographic areas, especially over the QTP, which might be related to the complex topography of the QTP.

5. Conclusions

The ERA5-Land precipitation data overestimated the observed precipitation in the Qilian Mountains. The coefficient of determination (R2) between ERA5-Land precipitation data and observed precipitation data is 0.97, which indicates that ERA5-Land precipitation data have good applicability in the Qilian Mountains. According to the changes in correlation coefficients on the monthly, seasonal, and annual scale at all stations, ERA5-Land reanalysis precipitation data can capture the observed precipitation changes in the Qilian Mountains better than other datasets evaluated previously.
The annual scale precipitation bias between ERA5-Land monthly precipitation data and observed precipitation data may be caused by the altitude difference between Po and Pe (R2 = 0.2037). Precipitation bias increases with the increasing altitude difference between Po and Pe. Therefore, it is necessary to use the bias correction or downscaling method to reduce the bias between ERA5-Land reanalysis precipitation data and observations, which can improve the reliability of ERA5-Land reanalysis precipitation data. In general, ERA5-Land reanalysis precipitation data is reliable in the Qilian Mountains, but it still needs to be corrected or downscaled to improve its accuracy in the future.

Author Contributions

L.Q.: writing—original draft. P.Z.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research Project of Higher Education Institutions in Anhui Province, China (No. 2024AH051743).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The observed precipitation data used in this study are all publicly available data from the National Meteorological Information Center (NMIC), which provides meteorological data (https://data.cma.cn/, accessed on 1 July 2025). The ERA5-Land monthly averaged reanalysis precipitation data were downloaded from the ECMWF and C3S CDS (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=form, accessed on 1 July 2025).

Acknowledgments

We gratefully acknowledge the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the ERA5-Land reanalysis datasets used in this study. The data support provided by the Copernicus Climate Change Service (C3S) is also highly appreciated.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The spatial arrangement of 17 meteorological stations and ERA5-Land grid points across the Qilian Mountains.
Figure 1. The spatial arrangement of 17 meteorological stations and ERA5-Land grid points across the Qilian Mountains.
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Figure 2. Comparison of monthly mean precipitation at all 17 meteorological stations in the Qilian Mountains with ERA5-Land reanalysis precipitation in 12 months.
Figure 2. Comparison of monthly mean precipitation at all 17 meteorological stations in the Qilian Mountains with ERA5-Land reanalysis precipitation in 12 months.
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Figure 3. Scatter plot of the monthly mean precipitation of meteorological station observations from the QLM compared to the ERA5-Land precipitation.
Figure 3. Scatter plot of the monthly mean precipitation of meteorological station observations from the QLM compared to the ERA5-Land precipitation.
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Figure 4. Relationship of bias and elevation differences between the observed annual mean precipitation and ERA5-Land data during 1979–2017.
Figure 4. Relationship of bias and elevation differences between the observed annual mean precipitation and ERA5-Land data during 1979–2017.
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Figure 5. Relationship of bias and elevation differences between the observed seasonal mean precipitation and ERA5-Land data during 1979–2017.
Figure 5. Relationship of bias and elevation differences between the observed seasonal mean precipitation and ERA5-Land data during 1979–2017.
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Table 1. Comparison between the monthly averaged precipitation at each of the 17 meteorological stations.
Table 1. Comparison between the monthly averaged precipitation at each of the 17 meteorological stations.
No.Bias (mm)rRMSE (mm)
11.67 0.793 3.10
21.51 0.843 2.96
32.00 0.854 3.72
41.41 0.852 4.16
54.32 0.890 6.13
60.61 0.852 3.26
710.00 0.896 13.15
81.42 0.759 6.03
91.37 0.751 2.07
103.75 0.937 5.74
117.71 0.948 10.60
129.20 0.947 12.03
130.91 0.830 2.53
14−1.28 0.879 4.08
151.67 0.793 3.10
166.23 0.911 9.76
176.11 0.895 8.65
Averaged3.450.8615.95
Table 2. Comparison of ERA5-Land seasonal mean precipitation with observations at all 17 stations.
Table 2. Comparison of ERA5-Land seasonal mean precipitation with observations at all 17 stations.
No.Bias (mm)rRMSE (mm)
SpringSummerAutumnWinterSpringSummerAutumnWinterSpringSummerAutumnWinter
11.981.441.961.270.8650.7590.8620.3392.272.462.321.44
21.312.231.680.820.7850.6730.8690.3281.683.132.001.03
31.503.751.820.940.7840.6190.8420.4931.834.472.221.04
41.622.011.480.550.6830.6990.7170.6202.223.722.570.67
54.176.245.111.770.7130.7210.8630.4484.477.025.421.88
60.001.140.990.310.7770.5600.7440.3911.003.741.780.52
710.0314.6712.692.610.7220.5030.6730.40310.2616.1213.012.71
80.441.193.001.040.5230.5230.3590.3922.495.614.381.14
91.412.760.720.570.4820.5210.7830.4891.543.010.920.61
104.255.893.301.550.6010.4870.7780.4624.507.323.631.59
116.6315.616.442.150.3780.7600.7990.4217.0616.046.742.19
127.7617.988.202.890.5590.5800.7860.5657.9918.598.482.93
131.321.150.570.580.7940.6270.6070.4741.652.561.150.72
14−0.89−3.12−0.97−0.150.7170.7070.7540.6542.024.371.700.47
153.985.114.961.200.6040.4240.6200.5854.457.325.461.29
163.2212.886.252.570.5030.3890.6360.4374.3914.117.012.64
174.908.648.692.210.7760.8340.6290.6715.519.769.322.32
Averaged3.155.863.931.350.6630.6110.7250.4813.847.614.591.48
Table 3. Comparison of ERA5-Land annual mean precipitation with observations at all 17 stations.
Table 3. Comparison of ERA5-Land annual mean precipitation with observations at all 17 stations.
No.Bias (mm)rRMSE (mm)
11.670.7411.80
21.510.7591.63
32.000.6752.15
41.410.6301.78
54.320.6314.47
60.610.5891.14
710.000.45210.20
81.420.3982.21
91.370.5641.42
103.750.6323.95
117.710.6907.84
129.200.5769.32
130.910.7211.13
14−1.280.6771.66
153.810.5644.13
166.230.5756.45
176.110.7776.28
Average3.570.6273.97
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Qian, L.; Zhao, P. Assessment of ERA5-Land Reanalysis Precipitation Data in the Qilian Mountains of China. Atmosphere 2025, 16, 826. https://doi.org/10.3390/atmos16070826

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Qian L, Zhao P. Assessment of ERA5-Land Reanalysis Precipitation Data in the Qilian Mountains of China. Atmosphere. 2025; 16(7):826. https://doi.org/10.3390/atmos16070826

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Qian, Lihui, and Peng Zhao. 2025. "Assessment of ERA5-Land Reanalysis Precipitation Data in the Qilian Mountains of China" Atmosphere 16, no. 7: 826. https://doi.org/10.3390/atmos16070826

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Qian, L., & Zhao, P. (2025). Assessment of ERA5-Land Reanalysis Precipitation Data in the Qilian Mountains of China. Atmosphere, 16(7), 826. https://doi.org/10.3390/atmos16070826

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