Spatio-temporal Precipitation Patterns Research Articles

Spatiotemporal Patterns of Typhoon-Induced Extreme Precipitation in Hainan Island, China, 2000–2020, Using Satellite-Derived Precipitation Data

Spatiotemporal Patterns of Typhoon-Induced Extreme Precipitation in Hainan Island, China, 2000–2020, Using Satellite-Derived Precipitation Data

Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

In arid regions like Saudi Arabia, accurate precipitation data are crucial for water resource management and climate studies. However, satellite-based precipitation products (SPPs) can contain uncertainties, impacting their reliability. This study evaluated the accuracy of five high-resolution SPPs [IMERG-V06 variants, Tropical Rainfall Measuring Mission (TRMM)-3B42V7, and Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT)] over Saudi Arabia. We compared daily, monthly, and yearly precipitation estimates from SPPs with in situ rain gauge data (2010–2022) using both continuous and categorical metrics. The evaluation encompassed point-to-pixel comparisons, regional analysis, and national assessments. All SPPs effectively captured the spatiotemporal patterns of precipitation across the country. Notably, monthly estimates showed stronger agreement with rain gauge data than daily estimates, as indicated by higher correlation coefficients. IMERG products generally outperformed SM2RAIN-ASCAT and TRMM, with IMERG-LR exhibiting superior performance in estimating monthly precipitation. However, underestimation of light precipitation events (<2 mm/day) was observed across all SPPs. In addition, their ability to detect moderate and heavy precipitation events remained uncertain, requiring further investigation. While IMERG-FR showed reduced bias and root mean square error compared to IMERG-ER and IMERG-LR, its capability for precipitation event detection did not exhibit significant improvement. This study highlights the need for bias correction of IMERG-LR and IMERG-FR monthly estimates for improved application in hydrometeorological studies in Saudi Arabia. Our findings contribute valuable insights for both data users and SPP algorithm developers, aiming to enhance the accuracy and reliability of satellite-derived precipitation data in arid environments.

Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region.

Monthly precipitation prediction at regional scale using deep convolutional neural networks

AbstractVariations in monthly precipitation are associated with climate extremes having significant socio‐economic and eco‐environmental impacts. Knowledge of monthly precipitation information is therefore valuable for policy making. Extensive research has been conducted on dynamic prediction using state‐of‐the‐art coupled climate models. However, the skilful prediction of monthly precipitation with dynamical models remains a challenge. With the development of machine learning tools, statistical predictions show comparable performance with dynamic models, but they are limited to at‐site monthly prediction, due to the lack of ability for processing spatially connected geophysical data. To improve monthly precipitation forecasting and provide regional forecasts, we propose a model termed UNet‐RegPre based on convolutional neural network and U‐net architecture. The model shows comparable prediction skills with recurrent state‐of‐the‐art dynamic forecasts (CFSv2), and has the capability to capture spatiotemporal patterns of precipitation and reproduce the main process of representative droughts. The key precursors for rainfall development identified by UNet‐RegPre shows that the constructed model can detect the climatic connection between rainfall in eastern China and summer monsoon, showing the potential to advance hydrometeorological understanding with a deep learning‐based model. These results suggest that the proposed model can be a potential tool for precipitation prediction.

Spatio-temporal patterns of precipitation in arid and semi-arid regions in western India

Spatio-temporal patterns of precipitation in arid and semi-arid regions in western India

Seasonal variations in surface processes and hydroclimate on an alpine lake, NE Tibetan Plateau

Koucha Lake, located in Qinghai Province, north-eastern Tibetan Plateau (NETP) at an elevation of 4530 m a.s.l. Occupies an ideal position to investigate the seasonal hydroclimatic variations and related surface processes during the Holocene. We re-investigated the lake and analysed a new sediment core from the deepest part of the lake. Grain size measurements and related endmember modelling analyses (EMMA) in combination with ostracod associations together with geochemical parameters and mineral compositions from core sediments were used as relevant proxies for hydro-climatic reconstruction.A comparison of δ18O/δD from various waters on the NETP indicates a clear depleted isotopic signature of non-monsoon moisture supply in comparison to summer rainfalls, highlighting the summer evaporation for δ18O enrichment, as a reference for interpreting δ18O values from lake sediments for various situations of hydrological balance. Based on the individual core chronology the multi-proxy data indicate a very low sedimentation rate over the entire period in accordance with the catchment topography and the absence of well-developed and constant river systems, limiting the flux of sediment supply to the lake. Thus, Koucha Lake remains almost a closed system since the Lateglacial, except for the early mid-Holocene part of the sequence. The coarser particle components and their representative endmembers for spring flood and permafrost thaw between 11.9 and 10.1 cal Ka BP and from 4.5 to ca. 1.0 cal Ka BP point to lower lake levels, influenced by long winter duration with dry summer interruptions, and lighter oxygen isotopes were preserved at low evaporations. Fine-grained sediments supplied by permafrost thaw associated with activating summer surface erosion led to lake level rise between 10.1 and 4.5 cal Ka BP and during the last 1000 years, with a maximum during 7.6–7.1 cal Ka BP. Their enriched δ18O values indicate extended summer duration and concomitant evaporation enhancement, albeit a positive hydrological balance. We suggest that the interannual latitudinal shifts of the intertropical convergence zone (ITCZ) and westerly Jet and attendant varying monsoon rain belt locations are responsible tracers for the variation of the hydrological budget of the lake and the variable spatio-temporal precipitation patterns in humid and (semi) arid regions in China.

Evaluation of coupled model intercomparison project phase 6 models in simulating precipitation and its possible relationship with sea surface temperature over Myanmar

The study investigated the precipitation variability over Myanmar at the annual and seasonal scales by comparing 12 model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with gridded observational data provided by the Global Precipitation Climatology Centre (GPCC) from 1970 to 2014. Using Mann–Kendall and Sen’s slope estimator, the trend analysis was assessed. Correlation analysis was also used to investigate the relationship of observational and Ensemble means precipitation with sea surface temperature (SST) anomalies. Results show a better correlation pattern of ENS with observation precipitation than that of individual selected models during the May-October season than that of the annual scale. Meanwhile, UKESM1-0-LL, NESM3, and HadGEM3-CC31-LL show high correlation with a relatively low root-mean-square difference. A few models roughly capture the spatiotemporal patterns of precipitation during MJJASO over Myanmar. The root mean square errors (RMSEs) of MIROC6, CNRM-ESM2-1, CNRM-CM6, and NESM3 are lower than that of ENS, whereas the RMSEs of CESM2, GFDL-CM4, HadGEM3-CC31-LL, GFDL-ESM4, UKESM1-0-LL, MPI-ESM1-2-HR, MRI-ESM2-0, and IPSL-CM6A-LR are higher than that of ENS, for annual precipitation. Heterogeneous correlation coefficients and slope changes are evident within the country at both annual and seasonal periods. Overall, the ENS showed a long-term increasing annual trend. Most of the model exhibited increasing annual trends while some showed decreasing annual trends. The correlation between the annual series and SST anomalies shows stronger correlation coefficient than that of seasonal. Overall, the correlation analysis of the SST anomalies reveals significant positive and negative relationships with the ENS precipitation. We recommend considering future projections of precipitation changes over Myanmar in future work.

Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda

Abstract. Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall RD3 to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t−3) was the most informative hydrological variable for landslide hazard assessment (AUC ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and RD3 following the cause–trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ∼20 % at the expense of a minor reduction in true alarms by ∼8 %.

Influence of low frequency variability modes on spatiotemporal patterns of temperature and precipitation in Northwestern North America

ABSTRACT This study performs a comprehensive analysis of the influence of low-frequency variability modes (LFVMs) on the average and extreme temperature and precipitation in the extended winter (November–February) over Northwestern North America (NWNA). Maximum covariance analysis is applied to quantify the covariability between LFVMs and regional climate variables, followed by composite analysis to assess the influence of each LFVM on extremes at local scales using high-resolution gridded observations. This study extends previous analyses by characterizing the spatial patterns of regional climate variations associated with a suite of LFVMs. This includes the associations between negative phases of North Atlantic Oscillation (NAO) and extreme precipitation over western NWNA and the central Columbia basin and the positive phase of the North Tropical Atlantic (NTA) with increases in the minimum and maximum temperature of NWNA except for the western Fraser basin. Overall, the analyses show that LFVM effects are more pronounced over the eastern and southcentral parts of NWNA during extended winter.

Spatiotemporal Changes in Precipitation during the Summer Maize Growing Season in the North China Plain and Analysis of Its Causes

The North China Plain is an important summer maize production region in China. Investigating spatiotemporal variation patterns of precipitation during the summer maize growing season will guide the prevention of droughts and floods and ensure food production. Daily precipitation data during the summer maize growing season in the North China Plain from 1960–2020 were used to analyze spatiotemporal changes in precipitation, examine the migration patterns of precipitation barycenters, and quantitatively analyze the effects of ENSO (El Niño-Southern Oscillation) warm and cold events on precipitation variation characteristics. Results revealed that in the past 61 years, precipitation showed an insignificant decreasing trend; however, there were considerable differences detected in the spatial distribution layouts of precipitation between different developmental stages. The precipitation distribution layout during the sowing–jointing stage was mainly “North–South”, the zero contour was near 36° N, and the other developmental stages were mainly “global” with phases that were the opposite of one another. Moreover, the precipitation barycenter during the jointing–flowering stage showed a significant southward migration. Precipitation during the three developmental stages negatively correlated with warm events, precipitation during the flowering–maturation stage positively correlated with cold events, the relationship between precipitation changes during warm and cold events and the intensity of warm and cold events was not significant, and Pacific Decadal Oscillation (PDO) was the main climatic factor that affected precipitation changes during the summer maize-growing season in the North China Plain.

Spatiotemporal patterns of precipitation based on the Bayesian maximum entropy method in a typical catchment of the Heihe River watershed, northwest China

ABSTRACT Precipitation patterns are vital to water resource management and hydrological research, especially in the upper reaches of inland rivers in arid and semiarid areas. However, estimating spatiotemporal precipitation patterns at a basin scale is challenging due to limited observations. In this study, spatiotemporal patterns of precipitation amount, frequency, duration, and intensity at different time scales from 2014 to 2019 are estimated using the Bayesian maximum entropy method in the Tianlaochi catchment of the Heihe River watershed, northwest China. The study's results show that the annual average precipitation amount was 535.9 mm from 2014 to 2019, with precipitation amount between May and September accounting for 85.9% of the annual precipitation amount. For daily precipitation, the average frequency rate of light precipitation is highest at 59.55%, however, the average contribution rate of moderate precipitation is highest at 50.33%. The spatial distribution of precipitation is characterized by high-value areas concentrated in the central valley and low-value areas located at the catchment's outlet. The most important driving factors of precipitation patterns are elevation, relative humidity, and wind direction. These outcomes can be used to establish accurate hydrological models in the catchment and provide support for water resource management in the Heihe River watershed.

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes.

Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data.

Multiple trend tests on air temperature and precipitation anomalies in Vietnam

Abstract An informed decision on building climate resilience and adaptation requires a deep understanding of long-term changes in temperature and precipitation, especially for most vulnerable countries to climate extremes like Vietnam. Hence, this study aimed to comprehend spatiotemporal trend possibilities of temperature and precipitation in Vietnam by employing multiple non-parametric statistical trend tests. This study also suggested a modified procedure, represented as a Hovmöller-like diagram, for enhancing the characterization of long-term spatiotemporal trends by applying multiple monotonic trend tests to all latitude coordinates through all possible 30-year periods. The superiority of multiple trend tests over a single one is that this methodology can assess the sensitivity of trend test results to the beginning years, ending years, and record lengths, thus emphasizing the necessity of performing monotonic trend tests repeatedly. The results show consistent warming trends in all climate sub-regions over the last 4–5 decades, with the estimated trend slopes varying from approximately 0.010–0.042 °C/year. Precipitation anomalies in most climate sub-regions, especially in the southern part, experienced significant increasing trends at the rate of around 0.29–2.76%/year during the last 3–5 decades. These findings are expected to contribute more insights into the spatiotemporal trend patterns of temperature and precipitation in Vietnam.

Developing machine learning models with multisource inputs for improved land surface soil moisture in China

Accurate and spatially continuous land surface soil moisture (SSM) data will greatly benefit analyses of heat transfer, energy exchange and agricultural dryness. To obtain spatiotemporally consistent SSM information, five machine learning (ML) models, i.e., polynomial regression (PR), ridge regression (RR), lasso regression (LR), elastic net regression (EnR) and random forest regression (RfR) models, were generated to map the regional SSM in the 0–10 cm soil layer across the study area. Multiple features, including the geographical location, elevation, vegetation coverage, soil texture, seasonal patterns and satellite-retrieved SSM product from Fengyun-3C (FY-3C), were selected as the input variables for the proposed ML models. In situ SSM measurements from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) were used as the reference dataset. The error metrics, including the coefficient of correlation (R), mean relative error (MRE), unbiased RMSE (ubRMSE) and mean absolute error (MAE), between the measured SSM values and those estimated using the different models were calculated. Among those ML models, the RfR model showed the best performance in the training (R = 0.981, MRE = 7.3%, ubRMSE = 0.021 cm3/cm3, and MAE = 0.015 cm3/cm3) and testing (R = 0.789, MRE = 22.2%, ubRMSE = 0.065 cm3/cm3, and MAE = 0.047 cm3/cm3) processes and was applied to map the regional SSM values and measure the importance of each input feature. The results indicated that geographical location, i.e., latitude (35.84%) and longitude (16.96%), contributed the most to the SSM estimation model, followed by elevation (14.88%), vegetation coverage (9.75%), the FY-3C SSM product (8.30%), the soil texture (8.04%) and seasonal patterns (6.23%). In addition, the SSM estimations across mainland China matched the spatiotemporal patterns of historical precipitation well, which indicated the feasibility of achieving accurate and consistent land surface (0–10 cm) soil moisture monitoring results using the established RfR model with appropriately selected input features.

Characterizing the features of precipitation for the Tibetan Plateau among four gridded datasets: Detection accuracy and spatio-temporal variabilities

Evaluation of the gridded products in the detection accuracy and spatio-temporal variabilities of precipitation over the Tibetan Plateau (TP) is crucial to the research of climate change and extreme events. Previous evaluation works mainly focus on the amount of precipitation and the capability in event detection. This study presents a multi-scale evaluation of four gridded precipitation datasets with relatively high resolutions against stations data in the variabilities of precipitation over the TP from 2004 to 2017. The four gridded products are the fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5), the Global Land Data Assimilation Systems (GLDAS), the High Asian Refined Analysis version 2 (HAR v2), and the Tropical Rainfall Measuring Mission (TRMM). Results show that TRMM and GLDAS have good performances in the occurrence frequency of daily precipitation over the TP but cannot precisely detect the daily precipitation events. In contrast, ERA5 can well detect the daily precipitation events although it overestimates the frequency of daily precipitation. Regarding the longer time scales, ERA5 outperforms the other three gridded products. It succeeds in reproducing the interannual and decadal variabilities of precipitation and reflecting the spatio-temporal patterns of precipitation over the whole TP. Thus, this study highlights that there are different optimal choices of precipitation products for different study purposes, and demonstrates the potential capability of ERA5 for being used in the studies of climate and hydrological change over the TP.

A novel method for ranking CMIP6 global climate models over the southeast Asian region

AbstractThe performance of 28 CMIP6 models in simulating the Southeast Asian (SEA) climate is investigated. An evaluation methodology is developed to evaluate spatiotemporal patterns of precipitation, near‐surface temperature, and 850‐hPa wind. Mean annual cycles of temperature and rainfall are assessed separately over Indochina, the Maritime Continent, and the Philippines; and the summer and winter seasons are selected for studying spatial patterns. Within these distinctions, statistics comparing the models to reference data are calculated. The patterns' shape and phase are notably studied, and wind direction differences are assessed specifically over the most major wind flows. We present a scoring procedure allowing us to give each model a unique score reporting on the diverse evaluation aspects, hence establishing a ranking. The process rests on a scoring function growing exponentially with the performance: it favours the models combining good performances according to the largest number of criteria. The assessment highlights EC‐Earth3 and EC‐Earth3‐Veg, which conduct excellent simulations of the regional patterns of rainfall and wind. Their temperature outputs are also reliable, but CNRM‐CM6 ranks first for this variable, while finishing third globally. Local differences are discussed: we emphasize, among other things, the diverse impact of topography on temperature and rainfall, or the frequent deviation of winter winds circulating from the South China Sea to the Java Sea. In addition, a similarity study reveals major difficulties for simulating near‐equatorial annual cycles of temperature and precipitation whereas the summer monsoon spatial rainfall patterns bring out the largest discrepancies. In any event, the top models pointed out by our ranking constitute a high‐performance subset that further climate modelling experiments in SEA can rely on.

Precipitation in surrounding mountains instead of lowlands facilitated the prosperity of ancient civilizations in the eastern Qaidam Basin of the Tibetan Plateau

Precipitation fluctuation has been suggested as a crucial influencing factor in the evolution of ancient civilizations across the world. Nuomuhong culture (~3400–2450 BP) and Tuyuhun (313–663 CE) were unique ancient civilizations that developed in the eastern Qaidam Basin of the north Tibetan Plateau. However, how precipitation variation in the mountain–basin system has affected the rise and fall of these two ancient civilizations is not well understood. Based on analyses of grain size, magnetic susceptibility, Rb/Sr ratio, loss on ignition, and optically stimulated luminescence dates from an aeolian deposit sequence, lowland climate change in the Qaidam Basin during the late Holocene is reconstructed. The Rb/Sr ratio varies from 0.31 to 0.33 during ~2500–2300 BP, it increases from 0.33 to 0.44 during ~2300–1600 BP, and decreases from 0.44 to 0.29 during ~1600–1400 BP, suggesting the climate was relatively dry during ~2500–2300 BP and ~1600–1400 BP and relatively wet during ~2300–1600 BP in the lowlands of the eastern Qaidam Basin, which corresponded well to the gap between the Nuomuhong and Tuyuhun periods. However, the prosperous periods of the Nuomuhong and Tuyuhun were synchronous with high precipitation in the surrounding mountains. Therefore, high precipitation in the surrounding mountains nourished the expansion of oases and then facilitated the development of Nuomuhong and Tuyuhun, which was inconsistent with precipitation variation in the lowlands of eastern Qaidam Basin. The spatiotemporal pattern of precipitation in the mountain–basin system of the Qaidam Basin during the late Holocene was likely affected by solar activity through uplifting–subsiding air flow mechanism, with more precipitation in the mountain and less precipitation in the basin under higher solar activity. A cognitive model is provided to illustrate how solar activity affected the spatiotemporal patterns of precipitation in the mountain–basin structure of the eastern Qaidam, and then human settlement and civilization evolution during the late Holocene.

Interannual Climate Variability and Malaria in Mozambique

AbstractMalaria is among the greatest public health threats in Mozambique, with over 10 million cases reported annually since 2018. Although the relationship between seasonal trends in environmental parameters and malaria cases is well established, the role of climate in deviations from the annual cycle is less clear. To investigate this and the potential for leveraging inter‐annual climate variability to predict malaria outbreaks, weekly district‐level malaria incidence spanning 2010–2017 were processed for a cross‐analysis with climate data. An empirical orthogonal function analysis of district‐level malaria incidence revealed two dominant spatiotemporal modes that collectively account for 81% of the inter‐annual variability of malaria: a mode dominated by variance over the southern half of Mozambique (64%), and another dominated by variance in the northern third of the country (17%). These modes of malaria variability are shown to be closely related to precipitation. Linear regression of global sea surface temperatures onto local precipitation indices over these variance maxima links the leading mode of inter‐annual malarial variability to the El Niño‐Southern Oscillation, such that La Niña leads to wetter conditions over southern Mozambique and, therefore, higher malaria prevalence. Similar analysis of spatiotemporal patterns of precipitation over a longer time period (1979–2019) indicate that the Subtropical Indian Ocean Dipole is both a strong predictor of regional precipitation and the climatic mechanism underlying the second mode of malarial variability. These results suggest that skillful malaria early warning systems may be developed that leverage quasi‐predictable modes of inter‐annual climate variability in the tropical oceans.

A New Perspective for Charactering the Spatio‐temporal Patterns of the Error in GPM IMERG Over Mainland China

AbstractEvaluation is necessary and informative for processing and applying the precipitation products. However, researchers mainly focus on the statistical indicators of satellite‐based precipitation evaluation over manually determined regions without further considering the geographical information. To reveal the potential connections between geographical features and the error patterns of satellite precipitation estimates, this study focuses on proposing a new perspective for charactering the spatiotemporal patterns of errors in the Integrated Multi‐satellitE Retrievals for global precipitation measurement (GPM; IMERG) based on China Merged Precipitation Analysis over mainland China in the period from 2012 to 2018. We applied a quantitative cluster analysis method to analyze the characteristics of the errors in GPM IMERG based on three geographical features including elevation (Ele), latitude (Lat), and the distance from seashore (DFS). Additionally, the analysis was conducted in both warm and cold seasons to capture the temporal error patterns in IMERG. The results show that: (1) IMERG has the ability to capture spatiotemporal precipitation patterns over mainland China, with relative overestimations mainly over southeastern region; (2) geographical features (Ele, Lat, and DFS) have strong linear relationships with the statistical error indexes (e.g., correlation coefficient) of IMERG; and (3) the spatial patterns of errors in IMERG were automatically determined and demonstrated significant geographical characteristics. Finally, results of this study would provide valuable references for improving the quality of the IMERG over land.

Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison

This study analyzes the performance of precipitation estimates from historical runs of the CMIP6 (Climate Model Intercomparison Project Phase 6) over the climatic regions of Iran. In order to capture the spatio-temporal precipitation patterns, using a set of evaluation metrics, 12 GCMs (General Circulation Models) are compared to the observation data from the GPCC (Global Precipitation Climatology Centre) at the common 1° spatial resolution for 1950–2014. A comprehensive assessment is performed at different temporal scales including monthly, seasonal and annual. Results indicate that the reliability of precipitation estimates varies significantly across space and time. The CMIP6 models best reproduce the climatological features of precipitation and its spatio-temporal changes over the arid and hyper arid areas of the country. The outputs of the models exhibit less systematic biases in the arid zone. In addition, a strong underestimation is detected throughout the rainiest zone, indicating high uncertainty in wet regions. All models tend to show some level of underestimation in summer months with the lowest rainfall. The findings illustrate substantial inter-model variability over different climatic zones. Each of the CMIP6 models appears to be more suitable in a specific climatic zone. The models that performed reasonably well in the humid zone (CNRM-CM6-1 and MRI-ESM2-0), did not perform well in the hyper arid and arid zones. Similarly, models (HadGEM3-GC31-LL, BCC-CSM2-MR, and CanESM5) that performed well in the arid and hyper arid zones did not perform as well in the humid zone. Results inform what types of models are suitable for different climate zones.