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The present study investigates the skills of CORDEX-CORE precipitation outputs in simulating Africa’s key seasonal climate features, emphasizing the added value (AV) of the dynamical downscaling approach from which they were derived. The results indicate the models’ good skills in capturing African rainfall patterns and dynamics at satellite-based observation resolutions, with up to 65.17% significant positive AV spatial coverage for the CCLM5 model and up to 55.47% significant positive AV spatial coverage for the REMO model. Unavoidable biases are however present in rainfall-abundant areas and are reflected in the AV results, but vary based on the season, the sub-area, and the Global Climate Model–Regional Climate Models (GCM-RCM) combination considered. The RCMs’ ensemble mean generally performs better than individual GCM–RCM simulations. A further analysis of the GCM–RCM model chain indicates a strong influence of the dynamical downscaling approach on the driving GCMs. However, exceptions are found in some seasons for specific RCMs’ outputs, where GCMs are influential. The findings also revealed that observational uncertainties can influence AV and contribute to a 6 to 34% difference in significant positive AV spatial coverage results. An analysis of these results suggests that the AV by CORDEX-CORE simulations over Africa depend on how well the GCM physics are integrated to those of the RCMs and how these features are accommodated in the high-resolution setting of the downscaling experiments. The deficiencies of the CORDEX-CORE simulations could be related to how well key processes are represented within the RCM models. For Africa, these results show that CORDEX-CORE products could be adequate for a wide range of high-resolution precipitation data applications.

The paucity of reliable ground based datasets remains a major challenge over Kenya. In the advent of extreme wetness or drought events, reliable precipitation estimates for local characterization is a long overdue process. In the present study, four Satellite derived Precipitation Estimates (SPE): TMPA V7 3B42, PERSIANN-CDR, CHIRPS, and ARC2, are assessed over four homogeneous zones in Kenya with gauge based data during 1998 -2016. Results show that variations of SPE products are based on complex geomorphology of different climatic zones. All SPE products depict bimodal annual precipitation pattern with west-east gradient representing heavier to lighter precipitation events. The Monthly analysis reveal good statistical agreement with reference datasets despite underestimation of precipitation in most regions. Seasonal precipitation events show that the PERSIANN-CDR perform better along low altitude humid climate and western zones around Lake Basin while ARC2 has uniform performance as gauge stations over highlands regions. Strong positive linear relationship on annual scale is evident in most SPE products with CHIRPS, ARC2, and TMPA exhibiting relatively high correlation (r) and minimum root mean squareerror (RMSE), except for PERSIANN-CDR. Overall, the findings of this study show the potentials of SPE products for applications over study domain. The TMPA V7 and PERSIANN-CDR could be useful in understanding individual floods events. Since the CHIRPS perform relatively well over ASAL regions, it could be utilized in monitoring droughts events.

This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June&ndash, July&ndash, August (JJA) and December&ndash, January&ndash, February (DJF) seasons for 1970&ndash, 2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models&rsquo, performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 &deg, C) was less than that of DJF (7 to 10 &deg, C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF.

Under global warming, ENSO-driven precipitation anomalies over central eastern Pacific shift eastward because of narrowing ENSO SSTA., During El Niño events, increased precipitation occurs over the equatorial central eastern Pacific, corresponding to enhanced convective heating that modulates global climate by exciting atmospheric teleconnections. These precipitation anomalies are projected to shift and extend eastward in response to global warming. We show that this predicted change is caused by narrowing of the meridional span of the underlying El Niño–related sea surface temperature (SST) anomalies that leads to intensification of the meridional gradient of the SST anomalies, strengthening boundary-layer moisture convergence over the equatorial eastern Pacific, and enhancing local positive precipitation anomalies. The eastward shift and extension of these anomalies also intensify and extend eastward negative precipitation anomalies over the tropical western North Pacific, by strengthening equatorward advection of low mean moist enthalpy. Changes in El Niño–induced tropical precipitation anomalies suggest that, under global warming, El Niño events decay faster after their peak phase, thus shortening their duration.

Global warming impacts leading to climate change is influencing all aspects of life and the ecosystems. Paris agreement capped the increase to a target below 2 °C and further downward trajectory to 1.5 °C above pre-industrial levels. This resolution requires nations to progressively monitor recent past and current temperature trends for accurate projections and adaptations. The present study characterizes recent temporal evolution of surface air temperature over Kenya during 1971–2010. The study utilized Climatic Research Unit reanalysis datasets for monthly minimum, maximum and mean average temperatures. Linear regression is utilized in determining statistical significance of trends, while Sequential Mann–Kendall rank test is employed in evaluating abrupt changes in temperature during the study period. Spatial analysis of mean temperature during the last four decades reveals evidence of rising temperature on annual and seasonal bases. Positive trends dominate western and central parts of Kenya, whereas insignificant positive trends are observed over northern and eastern parts of the study domain. Similarly, linear trends exhibit rising temperatures at the rate of 0.09 °C/decade, with an abrupt change in annual mean being detected in 1992. Overall, significant increase at 99% confidence level is observed during March–May season, while 95% confidence level is witnessed during October–December season.

The decline in March–May (MAM) ‘boreal spring’ rainfall is of great concern to Kenya’s agricultural sector. This study examines factors influencing MAM rainfall variability based on monthly observed and Climatic Research Unit (CRU) reanalysis rainfall datasets for the period 1971–2010. The distribution patterns of MAM rainfall were analyzed using Empirical Orthogonal Functions (EOF), whereas the Sequential Mann–Kendall (MK) statistic was used for trend analysis. Factors influencing seasonal rainfall were determined and assessment of the circulation anomalies associated with wet/dry condition during the study period was carried out. The MAM rainfall revealed decreasing trend. Wet years are associated with enhanced low-level convergence and upper-level divergence of winds, advection of moisture flux following a well-positioned and intensified Arabian high-pressure cell, and an accompanying rising branch of Walker circulation over Indian Ocean (IO). The sea surface temperatures anomalies (SSTAs) in the central and sub-tropical IO are closely related to MAM rainfall over Kenya. Positive SSTAs accompanying negative OLR anomalies may lead to intensified rising branch of the Walker circulation influencing the MAM wet events in Kenya. There is a negative correlation coefficient of − 0.62 between MAM rainfall and Outgoing Long-wave Radiation (OLR) indicating that the inter-annual variation of the MAM rainfall and OLR are in opposing phases and, hence, more convection and enhanced rainfall over the study domain. The study reveals that the circulation anomalies associated with the dry years are opposite to those in wet years, forming a good basis of monitoring similar events in future.

Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties.

This study aims to detect the primary precursors and impact mechanisms for January surface temperature anomaly (JSTA) events in China against the background of global warming, by comparing the causes of two extreme JSTA events occurring in 2008 and 2011 with the common mechanisms inferred from all typical episodes during 1979–2008. The results show that these two extreme events exhibit atmospheric circulation patterns in the mid–high latitudes of Eurasia, with a positive anomaly center over the Ural Mountains and a negative one to the south of Lake Baikal (UMLB), which is a pattern quite similar to that for all the typical events. However, the Eurasian teleconnection patterns in the 2011 event, which are accompanied by a negative phase of the North Atlantic Oscillation, are different to those of the typical events and the 2008 event. We further find that a common anomalous signal appearing in early summer over the tropical Indian Ocean may be responsible for the following late-winter Eurasian teleconnections and the associated JSTA events in China. We show that sea surface temperature anomalies (SSTAs) in the preceding summer over the western Indian Ocean (WIO) are intimately related to the UMLB-like circulation pattern in the following January. Positive WIOSSTAs in early summer tend to induce strong UMLB-like circulation anomalies in January, which may result in anomalously or extremely cold events in China, which can also be successfully reproduced in model experiments. Our results suggest that the WIOSSTAs may be a useful precursor for predicting JSTA events in China.

The present study proposes an assessment of the Added Value (AV) in seasonal mean temperature regional simulations by the Rossby Centre Regional Climate Atmospheric Model version 4 (RCA4) over West Africa, with emphasize on the contribution the driving Global Climate Models (GCMs) and the downscaling experiment. The results show that the AV by RCA4 varies depending on the driven GCMs, the season, and subareas of the study domain, while consistent performances are observed for specific GCMs. These results suggest that the key influencing factor of the quality of GCM driven RCA4 simulations is the consistencies between the GCMs and the RCA4 model physics. Our findings also point out that the RCA4's contribution to the AV results is influential, as it highly compensates the driven GCMs biases, although positive AV results are typically GCM dependent.

Understanding future evolution of drought scenario across localized domains remains an imperative process in bid to adapt tailor suit innovative solutions to drought risks and their impacts. The present study examines drought events by characterizing the trend, intensity, severity and frequency based on Standardized Precipitation Index (SPI), over Kenya, East Africa for near future (2010–2039), mid-century (2040–2069), and late century (2070–2100). The study utilizes Multi-model mean ensemble (MME) of five selected regional climate models (RCMs). Further, the models are bias corrected based on a quantile mapping bias corrected algorithm in order to minimize possible bias for accurate projections. The changes in annual and seasonal precipitation over Kenya is examined in order to associate with changes in drought occurrence. Results demonstrate positive shift, indicating an increase in projected rainfall change during all the three timescales. Projections of possible future meteorological drought events under RCPs scenario over study locale was conducted using SPI. The results demonstrate relatively better performance of biased corrected MME derived from Rossby Centre regional climate model (RCA4) in simulating drought indices over the Kenya. The MME projections for drought duration show an increase in moderate drought incidences with fewer incidences of extreme events across the RCP4.5 and 8.5 scenarios respectively. However, the duration of occurrences varies from one region to another with most hotspots located around northeastern sides of the country. Examination of projected changes in drought frequency and severity depict an occurrence of severe to extreme drought incidences that are expected to intensify during the near future time slice while overall projections show that more wet scenarios is depicted, with fewer cases of drought expected to occur during mid and towards end of the century of projection period. The study calls for enactment of appropriate mitigation measures to cope with possible scenarios of drought risks over Kenya in the future.

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March&ndash, May (MAM) and October&ndash, December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

This study assesses the performance of ten Regional Climate Model (RCMs) from the latest version of Rossby Centre of Atmospheric models (RCA4) in the simulation of precipitation over Greater Horn of Africa (GHA) from 1951–2005. The evaluation was performed against observed data from the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC). Results for mean seasonal analyses demonstrate an underestimation of March–May (MAM) and June–September (JJAS) precipitation whilst October to December (OND) precipitation is overestimated. Further assessment on the annual scale depicts underestimation of rainfall. However, the west to east gradient representing heavier to lighter precipitation and bimodal patterns of the north to south rainfall band is well captured by most models. The models fairly reproduce precipitation variability over the southeast region as compared to the northwest parts of the study domain. The mean ensemble invariably outperforms the individual RCA4 models due to its minimal probability deviance in precipitation in each zone and throughout the GHA region. The overall evaluation shows weak correspondence of the model data with observed CRU based on statistical metrics. The top five performing models are: MIROC5, CSIRO, CM5A-MR, MPI-ESM-LR, and EC-EARTH. Large variations of model performance are noted from one model to model, and from one region to the other. The ensemble mean of the outperforming RCMs reproduces the rainfall climatology over study domain with reasonable skill and the findings of this study will be a base for the study of extreme floods/droughts events in the region.