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Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of...
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... allowed us to assess the performance of the reanalysis products over East Africa in comparison to the rest of the continent. The detailed regional and local analysis was limited to the East Africa region from 25 • E to 50 • E and 13 • S to 17 • N (Figure 1). ...
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... Africa has a complex topography from low coastal areas up to peaks of 4500 m above sea level (Figure 1). It includes a multitude of climatic regions of tropical, arid, and moderate characters, with very different temperature and precipitation conditions [38]. ...
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... time series of annual precipitation aggregated within the East Africa box (13 • S-17 • N, 25 • E-50 • E) is shown in Figure 10a for observations and reanalysis products. Observations do not show a trend in annual mean precipitation. ...
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... time series shows that the similarity between ERA5 and observations increase over time, with the drying trend in ERA5 reducing the differences from observations. As there is large uncertainty in the trend of precipitation products in East Africa [64], we removed the time series' trends in Figure 10b. The detrended time series of ERA5 precipitation agree very well with observations, with a correlation of 0.84. ...
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... observed precipitation anomalies in 2005 show that East Africa was affected by dry conditions mostly in the Southern hemisphere (Figure 11a). All of Tanzania and most of Uganda and Kenya were drier than usual. ...
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... is consistent with the anomalous precipitation pattern associated with negative IOD years. Both ERA-interim and ERA5 capture a type of bimodal pattern with a normal to wet north and a dry south (Figure 11b,c). This suggests that both reanalysis datasets capture the apparent relationship between the negative rainfall anomalies over Equatorial and Southern East Africa and a negative IOD correctly. ...
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... shows an intensified picture of the observed conditions with slightly too wet conditions in Ethiopia and slightly too dry conditions in the southern hemisphere. For the year 1997, observations shows wet conditions in most of East Africa, with the strongest anomalies in East Kenya and South Somalia, with anomalies between 700 and 900 mm for the year (Figure 12a). This year corresponds to the strongest El-Niño of the century and a positive IOD event. ...
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... of these events led to above-normal rainfall over Southern Ethiopia and the Equatorial and Southern East Africa regions in the winter and spring seasons. ERA-interim and ERA5 reproduce these anomalously wet conditions over the coastal areas of East Africa (Figure 12b,c). However, ERA-interim shows dry anomalies in West Ethiopia and Uganda, and the strongest wet anomalies are located too far East and limited to the coastal areas of Somalia. ...
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... further narrowed down the analysis to the four focus countries Ethiopia, Kenya, Uganda, and Tanzania. Figures 13 and 14 show scatter plots of reanalysis data versus observations to allow for a more detailed investigation of the temporal temperature and precipitation distribution within the countries and also present the seasonal cycle of temperature and precipitation at the country level. ...
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... scatter plots of near-surface temperature in Figure 13 show that ERA5 is closer to observations than ERA-interim in all four countries, as the values are located much closer to the lines of best fit. While correlation ranges from 0.54 to 0.82 for ERA-interim, the range for ERA5 is from 0.78 to 0.93. ...
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... least good fit for temperature of both reanalysis products is found in Uganda, and even here ERA5 (0.78) is closer to observations than ERA-interim (0.54). Figure 13 also displays the annual cycle of near-surface temperature from CRU, ERA-interim and ERA5. Observations show the highest temperatures during late winter/early spring for Ethiopia, Kenya, and Uganda and during November for Tanzania. ...
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... scatter plots for monthly mean precipitation rates in Figure 14 show a similar change for temperature, which is closer to observations for ERA5 than for ERA-interim. As already seen in the African correlation maps, in East Africa the correlation between observed monthly mean precipitation and reanalysis is generally higher than the correlation between observed temperature and reanalysis. ...
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... annual cycles of precipitation from observations and the reanalysis datasets are displayed in Figure 14 (lower panels) and show that Ethiopia and Tanzania get their maximum rains during their respective summer seasons, whereas the countries around the equator (Kenya and Uganda) receive the peak rains during spring and autumn following the movement of the ITCZ. Both reanalysis products reproduce the observed seasonality of precipitation well despite the difference in amounts. ...
Citations
... The large-scale processes that promoted the development of the AR are analyzed using ERA-5 reanalysis data (Hersbach et al., 2020), which has a spatial resolution of 0.25°(∼27 km) and a temporal resolution of 1 hr. This state-of-the-art reanalysis data set has a good performance in the Middle East and North Africa in comparison with in-situ and satellite-derived measurements (Alghamdi, 2020;Fonseca et al., 2022b;Gleixner et al., 2020;Nogueira, 2020). ERA-5 is used to detect ARs using the same algorithm described in Guan and Waliser (2019), which employs a detection criteria based on intensity (above 85th percentile, or 100 kg m 1 s 1 , whichever is greater) and geometry (>2,000 km long, with a >2 length-to-width ratio) thresholds of IVT. ...
Plain Language Summary
Atmospheric Rivers (ARs) are narrow and long bands of high water vapor content, which largely originate in the tropics or subtropics and propagate into mid‐ and high‐latitudes. They can bring beneficial rain and snow but, in particular the most intense, can lead to catastrophic flooding and loss of life. One of such occurrences in the Middle East in mid‐April 2023 is investigated using model and observational data. The high‐resolution (2.5 km) simulation put in evidence narrow (5–15 km) and long (100–200 km) convective structures within the AR, known as AR rapids, which produced heavy precipitation (>4 mm hr⁻¹), further enhanced by gravity waves that developed over the high terrain in western Saudi Arabia, and propagated at high speeds (>30 m s⁻¹). ARs are occurring more frequently in the Middle East as they are globally, and with increased atmospheric water vapor in a warming climate, AR rapids may be even more destructive.
... It allows easy access to ERA5 climate datasets, allowing for quick and reliable microclimate predictions (Brambilla et al. 2024). The widespread use of the data set in global research studies reinforces its suitability for the HOA region (Gleixner et al. 2020). The thorough quality control protocols and validation processes have given researchers the confidence to use ERA5 data to assess SM drought characteristics in the region. ...
Droughts continuously threaten human life, livestock, and agriculture across the Horn of Africa (HOA). As climate change exacerbates drought frequency and severity, accurately quantifying spatiotemporal drought patterns is critical to developing evidence-based policies that mitigate impacts and build resilience among vulnerable communities. This study conducted a spatiotemporal analysis of soil moisture drought over the HOA, utilizing high-resolution ERA5 reanalysis data between 1951 and 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated at 3-, 6-, 9-, and 12-month timescales to identify historical drought events and assess drought duration and intensity changes over 70 years. Spatial analysis revealed decreasing soil moisture levels across HOA, with the most substantial reductions of 45% occurring in Djibouti and Northern Somalia. Comparisons between the baseline period (1951–1985) and the recent period (1986–2020) showed increasingly negative SPEI intensities, indicating a shift towards drier conditions, especially in Somalia, Kenya, and Ethiopia. The results also pointed to rising frequencies of moderate droughts by around 15% and severe droughts by 5–10% from 1986 to 2020 in the baseline period. The findings can inform policy to improve regional drought monitoring systems and the development of climate-resilient agriculture strategies, water resource management, and disaster risk reduction planning to protect lives and food security.
... The high resolution of spatial-temporal observed data is extremely important to understanding meteorological and hydrological investigation and the consequence of future climate variation at the basin and sub-basin level [29]. Reanalysis product provides optimized comprehensive and coherent global climate datasets for representing the climatology information and their inter-annual variability in the historical periods [41,42]. The Climate Hazard Group Infrared Precipitation with Stations (CHIRPS) was validated and evaluated in different regions of Ethiopia and showed high performance over others [43,44]. ...
The most suitable multi-model ensemble set of general circulation models is used to reduce the uncertainty associated with GCM selection and improve the accuracy of the model simulations. This study evaluated the performance of 20 global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing precipitation patterns over the Abaya-Chamo Sub-basin, Ethiopia. For the validation and selection of the models' capabilities, datasets from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) were used after comparing them with ground observational datasets. The objective was to identify the most suitable multi-model ensemble (MME) of a subset of CMIP6 GCMs to capture the rainfall for the 1981–2014 period over the region. Climate Data Operators (CDOs) were used in climate data processing and extraction, and the Mann-Kendall test and Theil-Sen slope estimator methods were utilized to analyze the trends of the CMIP6 simulations. Four statistical metrics (Nash-Sutcliffe coefficient, percent bias, normalized root mean square error, and Kling-Gupta efficiency) were used to further assess the performance of the models. A multi-criteria decision analysis approach, namely, the technique for order preferences by similarity to an ideal solution (TOPSIS) method, was used to obtain the overall ranks of CMIP6 models and to select the best-performing CMIP6 model in the region. The results indicated that CHIRPS and most of the CMIP6 simulations generally reproduced bimodal precipitation patterns over the region. The CESM2-WACCM, NorESM2-MM, NorESM2-LM, and NorESM2-LM models performed better than the other models in reproducing seasonal patterns for the winter, spring, summer, and autumn seasons, respectively. On the other hand, FGOALS-f3-L revealed the trends of the reference datasets for all seasons. In terms of the NSE, PB, NRMSE, and KGE metrics, EC-Earth3-C, EC-Earth3, EC-Earth3-C, and EC-Earth-C, respectively, were considered good at representing the observed features of precipitation over the region. EC-Earth3-C,EC-Earth3, EC-Earth3-Veg-LR, ACCESS-CM2, MPI-ESM1-2-HR, and CNRM-CM6-1-HR exhibited the best performances in the Abaya-Chamo Sub-basin.
Keywords
Abaya-Chamo sub-basin
CMIP6
Multi-model ensemble (MME)
A multi-criteria decision analysis
TOPSIS
... However, they suffer significant losses with regards to temporal and spatial resolution as well as information relating to local phenomena. Global reanalyses have inherent difficulties to provide fine-scale details that are often missed in the physics of the models or are meaningless at the low resolution considered (Bromwich et al., 2007;Kaiser-Weiss et al., 2015;Gleixner et al., 2020). ...
RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the Analog Method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities of broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
... One of the main challenges in using reanalysis or other types of global products for drought analysis is dealing with errors in these products compared to ground-based measurements (Ma et al. 2019;Fan et al. 2022). Despite extensive research on error analysis of global products for precipitation (Azizi et al. 2020;Saemian et al. 2021;Ghomlaghi et al. 2022;Fooladi et al. 2023), temperature (Gleixner et al. 2020;Rodrigues 2021;Zhang et al. 2021;Xu et al. 2023), evapotranspiration (Shirmohammadi-Aliakbarkhani and Saberali 2020; Elnashar et al. 2021;Ochege et al. 2021;Panahi et al. 2021;Zhu et al. 2022;Liu et al. 2023;Yao et al. 2023), and soil moisture (Li et al. 2020Wu et al. 2021;Fan et al. 2022;Huang et al. 2022;Sion et al. 2022), there are still research gaps when it comes to the combination of global products (Allies et al. 2022;Min et al. 2022). Many studies have evaluated combination models to increase the accuracy of multi-product approaches compared to single-product approaches (Fooladi et al. 2021(Fooladi et al. , 2023Jiao et al. 2021;Chen et al. 2022). ...
This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and
the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed
using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P,
T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions
to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.
... Zhu et al. (2021), Yilmaz (2023), and Rakhmatova et al. (2021) compared ERA5 temperature fields with individual ground stations in Antarctica, Turkey, and Uzbekistan, respectively. Conversely, Gleixner et al. (2020) and Roffe and van der Walt (2023) chose to compare ERA5 to some gridded observational datasets (CRU TS 4.02 [Harris et al., 2020] and NOAA CPC dataset). Both these datasets are an interpolation of station data to a regular grid with a fixed 0.5 step; additional interpolation is thus required to allow their comparison with reanalyses. ...
... Focusing on Europe, the work of Velikou et al. (2022) assessed the ability of ERA5 to reproduce both average temperature and temperature extremes, relying both on single stations and the E-OBS gridded dataset (Cornes et al., 2018). Velikou et al. (2022) report ERA5 advances but still advocate for the development of products with higher resolution and accuracy to satisfy impact-based studies (Gleixner et al., 2020), especially in countries with sea-land interaction and mountains like Italy. Similar studies have been carried out for the ERA5-Land reanalysis, but a comprehensive validation specifically over Italy has not been conducted so far. ...
Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications.
... Despite the real need for knowledge on the climatology of the NEB, the absence of long-term, high quality and flawless meteorological observations and the low density of weather stations pose an obstacle to this type of studies (De Pauw et al., 2000). To compensate for the lack of spatio-temporal data, other meteorological data sources have been developed and constantly used, such as satellite-based data, global and regional numerical forecast models, and atmospheric reanalysis, whose potential has already been explored in several studies (Pelosi et al., 2016;Negm et al., 2017;Chirico et al., 2018;Medina et al., 2018;Jiang et al., 2019;Gleixner et al., 2020;Longo-Minnolo et al., 2020;Vanella et al., 2020;McNicholl et al., 2021;Wu et al., 2022). Matsunaga et al. (2023) compared precipitation data from CPC/NOAA with those from meteorological stations in Bahia, affirming that CPC/NOAA data represent station observations well. ...
The climate of the Northeast Region of Brazil (NEB) has been intensively studied and analyzed for climate classification. The aridity index of the United Nations Environment Programme (UNEP) (AIUNEP) has been used for this purpose, but without fully satisfactory results. The input variables needed for its calculation are precipitation and reference potential evapotranspiration (ET0). However, although rainfall stations recording routine measurements of precipitation are well distributed in the NEB, they do not provide the necessary variables for estimating ET0. Thus, interpolation is used to calculate ET0, but this can generate errors. Another objective climate classification approach is the Thornthwaite method, based on the determination of the moisture index (Im), whose calculation also requires weather station data. Thus, seeking to circumvent the problem of paucity of stations and improve the spatial distribution of information on meteorological variables in the NEB, the present work had as one of its objectives to validate reanalysis data from ERA5 of the European Center for Medium-range Weather Forecast (ECMWF) and the unified gauge-based analysis of global daily precipitation project of the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA). After validation, climate classifications were developed for the NEB using the AIUNEP and Im. It was observed that the Thornthwaite climate classification overestimated the aridity in the NEB, while the IAUNEP tended to underestimate it. For this reason, a new climate classification index, called the absolute aridity index (Iab), was suggested, yielding satisfactory results.
... ERA5 provides crucial advantages, such as offering atmospheric variables for un-gauged locations, particularly in remote deserts and mountainous regions. It also accounts for the uncertainty in primary data and maintains temporal consistency (Gleixner et al. 2020). Furthermore, the long-term ERA5 data over 60 years offers extended temporal coverages of atmospheric and land factors. ...
Reliable estimation of reference evapotranspiration (ETo), an essential component of optimal irrigation management, is challenging in many regions due to its complex dependence on meteorological factors. Alternative empirical models, often used to estimate ETo considering data limitations, provide highly unreliable estimates for Iraq. This study aimed to formulate simpler empirical models for accurate ETo estimation with fewer variables in different climate regions of Iraq. The metaheuristic Whale Optimization Algorithm (WOA) was used to finetune the coefficients of the nonlinear least square fitting regression (NLLSF) model during development. Two simpler models were developed based on (1) only mean air temperature (T) (NLLSF-T) and (2) solar radiation and T (NLLSF-R) as inputs. The performance of the models was validated using historical ground observations (2012–2021), and the ETo was estimated using the Penman–Monteith method from the reanalyzed (ERA5) datasets (1959–2021). The models' spatial, seasonal, and temporal performance in estimating daily ETo was rigorously evaluated using multiple statistical metrics and visual presentations. The Kling-Gupta Efficiency (KGE) and normalized root mean square error (NRMSE) of the NLLSF-T model were 0.95 and 0.30, respectively, compared to 0.75 and 0.40 for Kharrufa, the best-performing temperature-based models in Iraq. Similarly, NLLSF-R improved the KGE from 0.78 to 0.97 in KGE and NRMSE from 0.44 to 0.22 compared to Caprio, the best-performing radiation-based model in Iraq. The spatial assessment revealed both the models' excellent performance over most of Iraq, except in the far north, indicating their suitability in estimating ETo in arid and semi-arid regions.
... Twenty-two vertical levels were retained from 1000 to 200 hPa, at 0.25 horizontal resolution. ERA5 was shown to better reproduce climate fields than previous reanalysis data over Africa (Gleixner et al., 2020). ...
Three‐hourly data from two satellite rainfall estimates products, PERSIANN and TMPA, are analysed to document the seasonal patterns of diurnal rainfall distribution over the Congo Basin and neighbouring areas. PERSIANN data for 2001–2017, at a one‐hour time‐scale, are further used to identify rain cells (≥4 mm·h⁻¹) in an attempt to explain the diurnal rainfall variations. Over land areas, an afternoon rainfall maximum is clearly shown, but over much of the region only a minor part of the rains (20%–30%) falls in the wettest 3‐h period. Substantial rains (often 50%–60%) occur in the evening and at night, as a progressively delayed peak from east to west, but a seasonal change is found in the meridional propagation of the peak diurnal rainfall, in a south‐westerly direction in January, and a north‐westerly direction in July. Rain cells have prominent genesis areas west of high terrain, but can develop over most regions, with a peak genesis time slightly ahead the diurnal phase of the rains. The size, mean lifetime and mean rainfall intensity of the rain cells are strongly related to each other and display a semi‐annual cycle not fully in phase with the seasonal cycle of the rains. The mean rain cell propagation speed (6.7 m·s⁻¹) is much lower than in previous studies, which focused on mesoscale convective systems. Rain cells which have a longer lifetime move much faster, the mean speed of those lasting less than 6 h being half that of those lasting at least 24 h. Most (86%) of the mobile rain cells propagate westward, but the meridional component of their propagation shows an annual cycle (southward in austral summer, northward in boreal summer) which matches the mid‐tropospheric winds and explains the seasonal changes in the diurnal rainfall peak.
... Rainfall data are available from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), and is considered an important source of rainfall data for various applications. Several studies (Gleixner et al., 2020;Saha et al., 2020;Singh et al., 2021;Wu et al., 2022, and references therein) have assessed the global ERA5 reanalysis. For example, Xu et al. (2019) and Tarek et al. (2020) investigated precipitation representation across the continental United States, evaluating its utility for hydrological applications in comparison to ERA5 and other reanalyses, along with observational data. ...
... This suggests the need to refine the temporal synchronization of rainfall lightning frequency. Gleixner et al. (2020) suggested that despite considerable advancements from ERA5-Interim, ERA5 still requires further improvement to offer more enhanced products with higher resolution and accuracy. Additionally, this study proposes significant improvements in ERA5 reanalysis rainfall, particularly in plain and mountainous terrains at flash time. ...
The ability of numerical weather prediction models to accurately predict extreme weather events, such as thunderstorms marked by heavy rainfall and lightning activities, has consistently been of great importance for human life. The objective of this study is to assess the long‐term reliability of the European Centre for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) rainfall in comparison to the Indian gauge‐adjusted Global Satellite Mapping of Precipitation (GSMaP_ISRO) rainfall at the time of lightning flashes measured by the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission satellite over the Indian region during the years 2001–2014. This analysis will provide valuable insights into the intricate relationship between lightning flashes and precipitation under various terrain conditions (low, mid, or high), across oceanic regions (Bay of Bengal, Arabian Sea), and during different monsoon phases (normal, active, or deficit). According to a prolonged examination of LIS data, April–June accounts for ~50% of the total flashes, with the largest number of flashes occurring over the Himalayan and the northeastern part of India. According to hourly GSMaP_ISRO rainfall, the most substantial lightning‐associated rainfall happens an hour prior to lightning flash (T − 1) and within three hours after (T + 3), indicating a robust correlation between heavy rainfall and lightning activity during this time frame. The rainfall in the ERA5 reanalysis misses the intensity as well as duration of the peak rainfall at the time of lightning flashes. Furthermore, the ERA5 reanalysis rainfall depicts under (over)‐estimation of rainfall in plain (orographic) regions. The underestimation of ERA5 rainfall is very pronounced over the Indian Ocean and Bay of Bengal regions, mainly between flash time (T) to two hours after the flash time (T + 2). The results indicate that there is a requirement for additional enhancements in the ERA5 reanalysis rainfall for lightning occurrences.
