Search Results (275)

Most existing precipitation data fusion methods rely on reliable precipitation values, such as those observed from ground-based rain gauges, to correct the satellite precipitation estimates (SPEs) that often involve systematic biases. However, such reliable data are rarely available in many regions of the world, especially in rugged terrain and hostile regions, rendering the correction suboptimal. To address this limitation, we propose a novel data fusion method—Triple Collocation Spatial Autoregression under Dirichlet distribution (TCSpAR-Dirichlet)—which eliminates the need for reliable data while still having the capability to effectively capture true precipitation patterns. The key idea in our method is using the variance of the precipitation estimates at each grid location obtained from each satellite to optimally leverage the associated satellite’s weight in data fusion, then characterizing the weights on all locations by a spatial autoregression model, and finally using the fitted weights to fuse the multi-sourced SPEs at all grid locations. We apply this method to SPEs in Nepal, which does not have ground gauges in many of its mountainous areas, to collect reliable precipitation data, to produce a fused precipitation dataset with uniform spatial coverage and high measurement accuracy. Full article

In recent years, satellite products have played an increasingly significant role in monitoring and estimating global extreme weather events, owing to their advantages of an excellent spatiotemporal continuity and broad coverage. This study systematically evaluates the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for the GPM Late Run (IMERG-L) product for regional precipitation events based on the observations in Zhejiang Province from 2001 to 2020. In this study, seven typical precipitation indices with seven accuracy evaluation indexes are applied to analyze the performance of IMERG-L from multiple perspectives in terms of the precipitation intensity, frequency and spatial distribution dimensions. The results show that IMERG-L is capable of capturing the spatial distribution trends, especially in the frequency-based precipitation indices (CWD, R10mm and R20mm), which can depict the regional wetness and precipitation pattern. However, the product suffers from a systematic overestimation in capturing heavy precipitation and an extreme precipitation intensity, with a high false alarm rate and unstable accuracy, especially in heavy rainfall and above class events, where the Probability of Detection (POD) drops significantly, showing an obvious reduction in the recognition capability and risk of misclassification. Specifically, IMERG-L failed to reproduce the observed eastward-increasing trends in the annual maximum precipitation for both one-day (RX1day) and five-day (RX5day) durations, demonstrating its limitations in accurately capturing extreme precipitation patterns across Zhejiang Province. Overall, furthering the optimization and improvement of IMERG-L in reducing the intensity-dependent biases in heavy rainfall detection, increasing spatial inhomogeneity in trend representations and improving the false alarm suppression for extreme events are needed for the accurate monitoring and quantitative estimation of high-intensity extreme precipitation events. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

Increasing water demand and climate change exacerbate water management challenges in arid and semi-arid regions experiencing water scarcity resulting from low and irregular precipitation and high evapotranspiration. These regions rely on substantial water storage capacity, typically provided by large multi-purpose public reservoirs and small private reservoirs. While public reservoirs are typically monitored, the number, size, and private ownership of small reservoirs complicate effective storage monitoring, hindering efforts to assess water availability during droughts and to allocate water efficiently and equitably. Remote sensing provides a solution to complement existing monitoring systems by offering high spatial and temporal resolution observations. This study introduces a methodology for monitoring the surface area of large and small reservoirs based on optical and radar images from Sentinel-1 and Sentinel-2 satellites. The Normalized Difference Water Index (NDWI) and the Otsu image segmentation method are employed to identify and estimate water body areas, and the Google Earth Engine and programming languages are used to automate the process. The validation results demonstrated correlation for most reservoirs, with slight underestimations at flood peaks. Among the 17 large reservoirs, 16 had an R² value above 0.82, 12 had an RMSE value below 0.8, and 14 had a KGE value above 0.7. For the small reservoirs, the method correctly identified 3224 of the 6370 reservoirs recorded in situ, with greater accuracy in the classes of reservoirs with elevation above 10 m. A total of 7251 reservoirs were mapped, including 4027 not present in the database of the responsible regulatory entity, most with an area of less than 1.8 ha. Performance was better for larger areas (>3 ha), while small areas were underestimated. This methodology offers a practical water management tool adaptable for various-sized surface water bodies, including small, unmonitored water bodies. Full article

Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months. Full article

This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events. Full article

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R²), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R² assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R², and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m³/s. The test results were 0.681, 0.91, 0.828, and 41.237 m³/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins. Full article

Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow simulation method using remote sensing precipitation data as input. By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. The method is demonstrated using GPM IMERG (Global Precipitation Measurement Integrated Multi-satellite Retrievals) remote sensing precipitation data for streamflow estimation in the upstream region of the Ganzi gauging station in the Yalong River basin of QTP for the period from 2010 to 2019. Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). Furthermore, ensemble simulations were compared: the Simple Average Method (SAM), Weighted Average Method (WAM), and the proposed 1D CNN method. The results revealed that, for the hydrological simulation of each individual models, the Kling–Gupta Efficiency (KGE) values during the validation period were 0.66 for BTOPMC, 0.71 for RF, and 0.74 for LSTM. Among the ensemble approaches, the validation period KGE values for SAM, WAM, and the 1D CNN-based nonlinear method were 0.74, 0.73, and 0.82, respectively, indicating that the nonlinear 1D CNN approach achieved the highest accuracy. The SHAP-based interpretability analysis further demonstrated that RF made the most significant contribution to the ensemble simulation, while LSTM contributed the least. These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research. Full article

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt’s Central Eastern Desert (CED), where a severe flash flood occurred on 26–27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 × 10⁶ m³, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements—Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 × 10⁶ m³ and 1 × 10⁶ m³) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga–Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management. Full article

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor parametric models, and multi-factor non-parametric models (Random Forest) were developed for three grassland types (alpine meadow, alpine grassland, and swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data and environmental factors, including climate and topography. A 10-fold cross-validation method was employed to assess the accuracy and stability of these models, and an AGB remote sensing inversion model was established to estimate the AGB of the Bayanbuluk Grassland from 2005 to 2024. Moreover, the BEAST mutation test, Theil–Sen median trend analysis, and Mann–Kendall test were used to analyse the temporal trends of AGB, identify the years of mutation points, and explore the changes in AGB across the entire study period (2005–2024) and at 5-year intervals, considering the influence of climatic factors. The results indicated that the machine learning (RF) model outperformed both multi-factor parametric and single-factor parametric models, with specific improvements in R² and RMSE across all grassland types. For instance, the RF model achieved an R² of 0.802 in alpine grasslands, outperforming the multi-factor parametric model with an R² of 0.531. The overall spatial distribution of AGB exhibited heterogeneity, with a gradual increase from northwest to southeast over the study period. Interannual AGB changes fluctuated significantly, with an overall increasing trend. Notably, from 2015 to 2019, 78% of the Bayanbuluk Grassland area showed a nonsignificant increase in AGB. Specifically, 46.7% of the alpine meadow AGB, 23% of the alpine grassland AGB, and 8.3% of the swampy meadow AGB showed non-significant increases. Further, temperature was found to be the dominant driver of AGB, with a stronger effect on alpine meadows and alpine grasslands than on swampy meadows. This is likely due to the relatively constant moisture levels in the swampy meadows, where precipitation plays a more prominent role. This study provides a comprehensive assessment of AGB trends, including both spatial and temporal analyses, which will inform future grassland resource management. Full article

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. Full article

Satellite remote sensing complements rain gauges and ground radars as the primary sources of precipitation data. While significant advancements have been made in spaceborne precipitation estimation since the 1960s, the emergence of multi-sensor precipitation estimation (MPE) in the early 1990s revolutionized global precipitation data generation by integrating infrared and microwave observations. Among others, Global Precipitation Measurement (GPM) plays a crucial role in providing invaluable data sources for MPE by utilizing passive microwave sensors and geostationary infrared sensors. MPE represents the current state-of-the-art approach for generating high-quality, high-resolution global satellite precipitation products (SPPs), employing various methods such as cloud motion analysis, probability matching, adjustment ratios, regression techniques, neural networks, and weighted averaging. International collaborations, such as the International Precipitation Working Group and the Precipitation Virtual Constellation, have significantly contributed to enhancing our understanding of the uncertainties associated with MPEs and their corresponding SPPs. It has been observed that SPPs exhibit higher reliability over tropical oceans compared to mid- and high-latitudes, particularly during cold seasons or in regions with complex terrains. To further advance MPE research, future efforts should focus on improving accuracy for extremely low- and high-precipitation events, solid precipitation measurements, as well as orographic precipitation estimation. Full article

Satellite-based precipitation products (SPPs) are essential for climate monitoring, especially in regions with sparse observational data. This study compares the performance of the latest version (V07B) and its predecessor (V06B) of the Integrated Multi-satellitE Retrievals for GPM (IMERG) across South America and the adjacent oceans. It focuses on evaluating their accuracy under different precipitation regimes in Brazil using 22 years of IMERG Final data (2000–2021), aggregated into seasonal totals (summer, autumn, winter, and spring). The observations used for the evaluation were organized into 0.1° × 0.1° grid points to match IMERG’s spatial resolution. The analysis was restricted to grid points containing at least one rain gauge, and in cases where multiple gauges were present within a grid point the average value was used. The evaluation metrics included the Root Mean Square Error (RMSE) and categorical indices. The results reveal that while both versions effectively capture major precipitation systems such as the mesoscale convective system (MCS), South Atlantic Convergence Zone (SACZ), and Intertropical Convergence Zone (ITCZ), significant discrepancies emerge in high-rainfall areas, particularly over oceans and tropical zones. Over the continent, however, these discrepancies are reduced due to the correction of observations in the final version of IMERG. A comprehensive analysis of the RMSE across Brazil, both as a whole and within the five analyzed regions, without differentiating precipitation classes, demonstrates that version V07B effectively reduces errors compared to version V06B. The analysis of statistical indices across Brazil’s five regions highlights distinct performance patterns between IMERG versions V06B and V07B, driven by regional and seasonal precipitation characteristics. V07B demonstrates a superior performance, particularly in regions with intense rainfall (R1, R2, and R5), showing a reduced RMSE and improved categorical indices. These advancements are linked to V07B’s reduced overestimation in cold-top cloud regions, although both versions consistently overestimate at rain/no-rain thresholds and for light rainfall. However, in regions prone to underestimation, such as the interior of the Northeastern region (R3) during winter, and the northeastern coast (R4) during winter and spring, V07B exacerbates these issues, highlighting challenges in accurately estimating precipitation from warm-top cloud systems. This study concludes that while V07B exhibits notable advancements, further enhancements are needed to improve accuracy in underperforming regions, specifically those influenced by warm-cloud precipitation systems. Full article

The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval. Full article