Global Precipitation Climatology Centre Research Articles

Intercomparisons of Three Gauge-Based Precipitation Datasets over South America during the 1901–2015 Period

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales.

Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran’s Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions.

Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Abstract Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

Global Future Climate Signal by Latitudes Using CMIP6 GCMs

AbstractThis study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.

Evaluation and bias correction of CRU TS4.05 potential evapotranspiration across vast environments with limited data

Long-term and reliable gridded estimates of potential evapotranspiration (PET) are often dearth. Being the longest available dataset with no validation, this work makes an effort to answer two questions: First, how well does the Climatic Research Unit (CRU) Time-Series (TS) Version 4.05 PET (CRU-PET) dataset capture FAO Penman-Monteith grass reference evapotranspiration (ETo) across a range of environments and, second, can a simple adjustment approach be devised to curb the error, if any? The CRU-PET dataset is evaluated against ETo in two data-scarce countries, namely Sudan and South Sudan. Monthly station data measured at 12 stations spread over hyper-arid, arid, semi-arid and dry sub-humid zones are used. It is shown generally that CRU-PET tends to perform better in dry than wet conditions, and in arid than in humid locations. In this regard, CRU-PET has a tendency to overestimate (underestimate) the stations' values in the arid (humid) zone, with best performance characteristics achieved north of latitude 15° N. Rainfall plays a key role in determining the bias and has a non-linear effect. This bias is, however, unclear in rainless months when both augmented overestimates and underestimates are observed. Incorporating geographical coordinates (latitude, longitude and altitude) as co-variables with monthly rainfall in a multiple linear regression explained 40.7% of the variations in the bias. In this data-scarce study area (>2.5 million km2), this simple adjustment significantly improves the stations' CRU-PET as indicated by eight performance metrics. Furthermore, validation analysis showed a reduction of the overall mean bias error based on the 12 stations from −0.255 to 0.086 mm/day. To apply this correction method on a spatial domain, gridded precipitation data are needed. We used the Global Precipitation Climatology Centre (GPCC 8) dataset, with which 41.9% of the CRU-PET bias is explained. In conclusion, this study cautions the use of CRU-PET dataset without prior evaluation in areas with similar geographical boundaries, climatic conditions and limited availability of data.

Future climate-driven drought events across Lake Urmia, Iran.

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016-2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985-2015) and future (2016-2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045-2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

Observational Uncertainty for Global Drought‐Pluvial Volatility

AbstractDroughts and pluvials have occurred in most regions in the past. However, what calls growing attention is the additive effects of these two opposite extreme events occurring in spatial‐temporal proximity to one another, sometimes beyond either of the hazards individually. This study examines the likelihood of global drought‐pluvial volatility which involves both meteorological drought‐to‐pluvial (DTP) and pluvial‐to‐meteorological drought transitions; meanwhile, identifies discrepancies and agreements among the widely used observations for such events, an aspect that remains currently overlooked. Globally, we find that the observation‐based data sets including Global Precipitation Climatology Center (GPCC), Climate Research Unit (CRU) and ERA5 reach a good agreement in estimating the event transition rates, with an average 15.46% (15.49%) of all meteorological droughts (pluvials) being succeeded by pluvials (meteorological droughts) in the following season. At the regional scale, our results reveal that the spatial variability and frequency associated with meteorological DTP transitions are slightly larger than that with pluvial‐to‐ meteorological drought transitions, but the observational uncertainty is more pronounced in the latter case as a result of greater uncertainty in the univariate drought depiction and enhanced regional divergence among the observed data. In general, GPCC and CRU exhibit higher consistency, albeit with less agreement under pluvial‐to‐ meteorological drought transitions, while ERA5 yields underestimations and reduced spatial variability considering both transition scenarios. The study highlights a need of using multiple independent observation‐based data sets for compound/multivariate extreme analysis, particularly in the context of climate‐related decision‐making, water resources planning, and future model validation studies.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Drought variability, changes and hot spots across the African continent during the historical period (1928–2017)

AbstractThe spatiotemporal variability of meteorological droughts, its changes and hot spots location across Africa are analysed for the period spanning 1928–2017 using the Standardized Precipitation Index (SPI) applied to the precipitation products from the Climatic Research Unit (CRU), University of Delaware (UDEL) and Global Precipitation Climatology Centre (GPCC). Spatially, an analysis based on rotational empirical orthogonal function identifies five regions of similar drought variability, namely the Sahel, East Africa, East Southern Africa, West Southern Africa and the Gulf of Guinea. Temporally, the most common periods of drought occurrence are the 1970s, the 1980s and, to a lesser extent, the 1990s. Changes in drought characteristics for the intermediate past (1958–1987) and recent past (1988–2017) compared to the far past (1928–1957) indicate robust increases of drought duration, frequency and severity in the Sahel, and to a lower extent in the Gulf of Guinea, some areas of Central Africa, part of Southern Africa and over Madagascar. These changes are stronger (weaker) along the Sahel during the intermediate past (recent past) and stronger (weaker) over Central and Southern Africa and Madagascar during the recent past (intermediate past). As a consequence, drought hot spots, mostly driven by severity during the regions' wet season, are identified in areas confined in the Sahel during the intermediate past and in regions mainly over Central and Southern Africa and Madagascar during the recent past. Our results are useful for drought disaster risk management across Africa and provide a valuable reference for future drought analysis under global warming conditions.

Extreme Drought Assessment in Sumatra-Indonesia Using SPI and EDI

Even though Sumatra is very vulnerable to the effects of drought, research identifying drought on this island is very limited, especially extreme drought. This research was conducted to identify extreme drought in Sumatra using the Standardized Precipitation Index (SPI) and the Effective Drought Index (EDI). This study uses precipitation data from the Indonesian Meteorology, Climatology and Geophysics Agency (BMKG) and Global Precipitation Climatology Center (GPCC) reanalysis data. The composite indices were conducted to discover some phenomena that cause the drought based on El Niño and positive Indian Ocean Dipole (IOD) events. The results showed that the El Niño and positive IOD phenomena were more likely to influence extreme droughts. However, the droughts in 2014 and 2008 tended to be influenced by the negative Sea Surface Temperature anomaly (SSTA). The spatial analysis results show that the areas that experience extreme drought more often are the west coast of Sumatra (except Aceh province), especially based on SPI12 and EDI. The composite indices results show that the drought that occurred in Aceh province was more influenced by the El Niño phenomenon in the December-January-February (DJF) period and the positive IOD in the June-July-August (JJA) – September-October-November (SON) period. In addition, Aceh province is an area that is not affected by El Niño-positive IOD, especially during the SON-DJF period. These results can be used to mitigate drought, especially when El Niño-positive IOD phenomena cause it.

Implications for Validation of IMERG Satellite Precipitation in a Complex Mountainous Region

Satellite-based precipitation retrievals such as the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), provide alternative data in mountainous regions. In this study, we evaluated IMERG in the Yarlung Tsangbo Grand Canyon (YGC) using ground observations. It was found that IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. We analyzed the relationships between the probability of detection (POD), false alarm ratio (FAR), bias in detection (BID), and Heidke skill score (HSS) and terrain factors. It was found that the POD decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the FAR was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. IMERG could capture the early morning peak precipitation in the YGC region but underestimated the amplitude of the diurnal variation. This bias was inherent at the sensor level, and the Global Precipitation Climatology Center (GPCC) calibration partially improved the underestimation. However, this improvement was not sufficient for the YGC region. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers.

Historical Trends and Characteristics of Meteorological Drought Based on Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index over the Past 70 Years in China (1951–2020)

Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the propagation patterns and trends of drought characteristics over a long timescale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC (the Global Precipitation Climatology Center) database was employed for data correction and gap-filling. Long-term drought evolution was assessed using both the SPI-12 (standardized precipitation index) and SPEI-12 (standardized precipitation evapotranspiration index) to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK (the Mann–Kendall) analysis method, along with the Theil–Sen slope estimator, was utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line, indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and widely distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country’s area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events.

Precipitation variability using GPCC data and its relationship with atmospheric teleconnections in Northeast Brazil

The present study investigates the influence of different atmospheric teleconnections on the annual precipitation variability in Northeast Brazil (NEB) based on the annual precipitation data from the Global Precipitation Climatology Center (GPCC) from 1901 to 2013. The objective of this study is to analyze the influence of different atmospheric teleconnections on the total annual precipitation of NEB for the 1901–2013 period, considering the physical characteristics of four subregions, i.e., Mid-north, Backwoods, Agreste, and Forest zone. To analyze the influence of different atmospheric teleconnections, GPCC data were used, and the behavior of the teleconnections was assessed using Pearson correlation coefficient, Rainfall Anomaly Index (RAI), and cross-wavelet analysis. The Pearson correlation was used to analyze the influence on the annual precipitation for the studied region. RAI was used to calculate the frequency of atmospheric patterns and drought episodes. The cross-wavelet analysis was applied to identify similarity signals between precipitation series and atmospheric teleconnections. The results of the Pearson correlation assessed according to Student's t test and cross-wavelet analysis showed that the Atlantic Multidecadal Oscillation (AMO) exerts a more significant influence on the Backwoods region at an interannual scale. In contrast, the Pacific Decadal Oscillation (PDO) exerts greater control over the modulation of the climatic patterns in NEB. The results of the study are insightful and reveal the differential impacts of teleconnections such as the AMO, PDO, MEI, and NAO on precipitation in the four sub-regions of NEB. The Atlantic circulation patterns strongly influence the interannual and interdecadal precipitation in the Agreste, Backwoods, and Mid-north regions, possibly associated with the Intertropical Convergence Zone (ITCZ) position. Finally, this study contributes to understanding internal climatic variability in NEB and planning of water resources and agricultural activities in such a region.Graphic abstract

Dominant modes of precipitation over Africa, and their associated atmospheric circulations from observations

AbstractThis study uses the empirical orthogonal function (EOF) analysis to identify and describe continental‐scale seasonal (MAM, JJA, SON and DJF) precipitation modes over Africa based on observation (Global Precipitation Climatology Centre; GPCC) and reanalysis (ERA5) data from 1982 to 2014. Using composite analysis, we attempt to identify the atmospheric circulations (wind and relative humidity) associated with each precipitation mode. Precipitation from ERA5 has a good agreement with that of GPCC, as observed in the good spatial congruence in the EOF analysis and precipitation composites. The EOF results for each season show that the loading patterns of Mode 1 (variance >20% for all seasons) match the long‐term mean precipitation distribution. Atmospheric conditions across the continent are primarily driven by the four main high‐pressure systems (Azores, St. Helena, Arabian and Mascarene High) that influence moisture distribution and subsequently modulate the seasonal rain belt distribution. Modes 2 (variance >11% for all seasons) and 3 (variance >10% for all seasons) are deviations of the leading mode associated with smaller‐scale atmospheric systems. However, large‐scale factors still dominate the overall precipitation pattern by modulating the seasonal energy transfer between the hemispheres. The results provide a comprehensive understanding of the continental‐wide African seasonal precipitation modes and the associated atmospheric circulation from observations. The findings can be used as a reference for future work using model data for historical and projection studies.

Evaluating CMIP6 Historical Mean Precipitation over Africa and the Arabian Peninsula against Satellite-Based Observation

This study evaluated the historical precipitation simulations of 49 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing annual and seasonal precipitation climatology, linear trends, and their spatial correlation with global SST across Africa and the Arabian Peninsula during the period of 1980–2014, using Global Precipitation Climatology Centre (GPCP) data as a reference. Taylor’s diagram was used to quantify the strengths and weaknesses of the models in simulating precipitation. The CMIP6 multi-mean ensemble (MME) and the majority of the GCMs replicated the dominant features of the spatial and temporal variations reasonably well. The CMIP6 MME outperformed the majority of the individual models. The spatial variation of the CMIP6 MME closely matched the observation. The results showed that at annual and seasonal scales, the GPCP and CMIP6 MME reproduced a coherent spatial pattern in terms of the magnitude of precipitation. The humid region received >300 mm and the arid region received <50 mm across Africa and the Arabian Peninsula. The models from the same modeling centers replicated the precipitation levels across different seasons and regions. The CMIP6 MME and the majority of the individual models overestimate (underestimate) in humid (arid and semi-arid)-climate zones. The annual and pre-monsoon seasons (i.e., DJFMA) were better replicated in the CMIP6 GCMs than in the monsoon-precipitation model (MJJASON). The CMIP6 MME (GPCP) showed stronger wetting (drying) trends in the northern hemisphere. In contrast, a strong drying trend in the CMIP6 MME and a weak wetting trend in the GPCP were shown in the Southern Hemisphere. The CMIP6 MME captures the spatial pattern of linear trends better than individual models across different climate zones and regions. The relationship between precipitation and sea-surface temperature (SST) exhibited a high spatial correlation (−0.80 and 0.80) with large variability across different regions and climate zones. The GPCP (CMIP6 MME) exhibited a heterogenous (homogeneous) spatial pattern, with higher correlation coefficients recorded in the CMIP6 MME than in the GPCP in all cases. Individual models from the same modeling centers showed spatial homogeneity in correlation values. The differences exhibited by the individual GCMs highlight the significance of each model’s unique dynamics and physics; however, model selection should be considered for specific applications.

A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data

The log, Box-Cox and log-sinh transformations have been widely used for the purpose of data normalization in hydrological applications. Focusing on the skewness and censoring characteristics of precipitation data, this paper has developed a novel Newton-type algorithm for the comprehensive implementation of the three transformations. Specifically, the likelihood functions are formulated by using the classic z-score and the Jacobian determinants of the three transformations; the gradient vectors are analytically derived to determine the searching directions; and grid-based starts are devised to facilitate the convergence to the global optimal. To test the effectiveness of the algorithm, numerical experiments are devised for monthly precipitation data from the Global Precipitation Climatology Centre. The results show that the log transformation reduces the positive skewness by using a simple offset parameter; the Box-Cox transformation is flexible for data normalization and incurs slightly high computational costs (more than 80 function evaluations); and the log-sinh transformation is computational efficient in normalizing precipitation data (less than 40 function evaluations). For the three transformations, multiple starts are in demand to handle local optimal and 10 grid-based starts for each parameter are shown to be sufficient for the algorithm to reach the global optimal. After transformation, skewness and kurtosis coefficients are reduced to nearly 0 in most regions and the Kolmogorov-Smirnov and Shapiro-Wilk tests suggest that transformed data follow Gaussian distribution at the significance level of 0.05. Overall, the Newton-type algorithm can serve to implement the log, Box-Cox and log-sinh transformations for effective hydrological modelling and water management.

Rainfall Variability and Trends in West Africa

Rainfall variability associated with climate change has enormous impacts on ecosystems, agriculture and people in West Africa but few studies have been devoted to it. Monthly rainfall data from 1901 to 2013, provided by the Global Precipitation Climatology Center dataset, were analyzed using segmentation and empirical modal decomposition (EMD) methods to increase our knowledge on past and recent spatio-temporal rainfall trends and their impacts on the West African region. The results obtained showed that the peak of rainfall during the short rainy season is observed in September in Côte d’Ivoire, Ghana and Liberia. The temporal variability of this rainfall is marked by several breakpoints whose durations range from 2 to 70 years. The periods of change in the rainfall regime, characterized by the appearance of breakpoints, vary from one country to another and are of unequal duration. The main breakpoint appears after 1960. Periods of relative or normal increase or decrease in precipitation are observed before and after 1960. The long-term variability of this rainfall is characterized by a decrease in the amount of rainfall over all West African countries. The results of this study can be used as a tool to help raise awareness among populations for sustainable management of water resources in response to climate change and its adverse effects.

Climatological spatial scales of meteorological droughts in China and associated climate variability

This study investigates the climatological spatial scales (CSSs) of meteorological droughts in China and the linkages to climate variability. The Global Precipitation Climatology Centre monthly gridded precipitation with a spatial resolution of 25 km × 25 km for 1961–2010 is used. The standardized precipitation index at different timescales (1-, 3-, 6-, 9-, 12-, 24-, 36-, and 48-month) is applied to characterize meteorological droughts. The CSSs of meteorological droughts are calculated using a method considering spatial correlation and anisotropy. The relationships between the CSSs and main monsoons and climate teleconnections in five selected regions are quantified using dynamic spatial panel models. The five regions are South China (SC), the Yangtze River Valley (YRV), North China (NC), Northeast China (NE), and the Tibetan Plateau (TIB). The following results are obtained: (1) The CSSs of multi-timescale meteorological droughts in China are determined. The means of the CSSs at various timescales in China are 68.9, 73.8, 71.8, 69.7, 68.8, 65.9, 62.9, and 60.8 × 104 km2, respectively. The CSSs in eastern China generally show a significant decrease with increasing timescale. (2) Significant quantification relationships (R2 greater than 0.85) suggest that there is not a simple linear relationship between climate anomalies and the CSSs, but rather a complex spatiotemporal interaction including exogenous and endogenous interaction effects. The CSSs not only strongly depend on their neighbors on the same timescale but also significantly depend on the CSSs of neighbors on adjacent timescales. Three monsoon indices (Indian monsoon, East Asian monsoon, and western North Pacific monsoon) have significant impacts on the multi-timescale CSS variations in the five regions, especially for NE, YRV, SC, and TIB, while the El Niño–Southern Oscillation and Pacific Decadal Oscillation affect mainly SC and YRV. These findings could broaden our understanding of the spatiotemporal relationships of droughts and be useful for drought risk management.

Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia

The performance for simulating precipitation of 27 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated over the Southeast Asia (SEA) region and nine SEA countries for the period 1975–2014 by comparing observation data from the Global Precipitation Climatology Centre (GPCC). The performance ranking of the GCMs was conducted with the ranking scores method using eight performance metrics that cover both spatial and temporal patterns. Compared to GPCC, the results show that some GCMs in CMIP6 reasonably capture the spatial precipitation patterns, with more in maritime and less in mainland. However, the output of most models presents considerable overestimation (wet bias) in Thailand, Cambodia, central Myanmar and maritime countries, and underestimation (dry bias) in Indonesia, Lao, northern Vietnam and western Myanmar. In addition, the findings illustrate that many models can reproduce the annual cycle shape and inter-annual variability which are consistent with GPCC; however, only 2 out of 27 models can detect increasing trends such as GPCC in every study domain. By model ranking, the best models vary from area domain to area domain. TaiESM1 performs best among the 27 GCMs over SEA region as well as Thailand. The model that has the best performance in most counties, i.e., Cambodia, Indonesia, Lao, and Vietnam is EC-Earth3-Veg-LR. EC-Earth3 is the best model in Brunei, Malaysia while CESM2-FV2 is the best model in Myanmar and the Philippines. It is also discovered that the mean ensemble of all GCMs has limited skills for all study domains. The results of this study can be used to support selection of the suitable models for simulating precipitation in specific study domains.