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Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC's) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province's five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings. [ABSTRACT FROM AUTHOR]

Sanjiang Plain is the most extensive marsh distribution region in China. Marshes in this region can protect biodiversity, regulate climate, and provide habitats for wild animals and plants. The normalized difference vegetation index (NDVI) is a crucial indicator of vegetation coverage, which may reflect ecosystem structure and functional features. Clarifying the spatiotemporal change of marsh coverage and its climatic drivers is vital for observing and predicting vegetation change in Sanjiang Plain. Using meteorological dataand MODIS NDVI data from 2000 to 2020, we analyzed the spatiotemporal variation in marsh vegetation coverage and climatic change effects in Sanjiang Plain. We found that the growing season vegetation NDVI of marsh increased significantly at a rate of 0.011/decade, indicating that the marsh vegetation growth has obviously improved during the past 21 years. Furthermore, we found that the increase of minimum (Tmin) and maximum (Tmax) temperature in July can significantly promote the marsh plant growth, and increasing nighttime Tmin has a stronger impact on promoting the growth than increasing daytime Tmax in this month. In addition, the increase of daytime Tmax in August can promote the marsh vegetation growth, whereas the increasing precipitation in August was unfavorable for the growth in Sanjiang Plain. [ABSTRACT FROM AUTHOR]

The study of rainfall in the long term is essential for climatic change understanding and socioeconomic development. The main goal of this study was to explore the spatial and temporal variations of precipitation in different time scales (seasonal and annual) in Cameroon. The Mann–Kendall and Pettitt tests were applied to analyze the precipitation variability. In temporal terms, the different regions of Cameroon have recorded significant drops in annual rainfall that Pettitt's test generally situates around the 1970s. The decreases observed for the northern regions of Cameroon are between −5.4% (Adamawa) and −7.4% (Far North). Those of western regions oscillate between −7.5% (South-West) and −12.5% (West). The southern Cameroon regions recorded decreases varying between −4.3% (East) and −5.9% (Center). In spatial terms, the divisions of the northern, western, and southern regions of Cameroon recorded after the 1970s (a pivotal period in the evolution of precipitation in temporal terms) indicate a precipitation decrease towards the South, the South-West, and the West. This study's findings could be helpful for planning and managing water resources in Cameroon. [ABSTRACT FROM AUTHOR]

Spatial and temporal variations in daytime and night-time precipitation and differences in these variations between the dry and wet seasons were investigated based on a daily precipitation dataset comprising data from 73 meteorological stations on the Yunnan–Guizhou Plateau from 1960 to 2017. The results show that both daytime and night-time precipitation and the numbers of daytime and night-time precipitation days exhibited nonsignificant downward trends over a long timescale but fluctuated on an interdecadal scale. A complicated relationship was found between elevation and precipitation because the vertical precipitation gradient first increased with elevation and then decreased with elevation when the elevation exceeded 1500 m. Additionally, the average annual precipitation was clearly greater at night-time than in the daytime during the corresponding period. Furthermore, heavy and moderate rainfall contributed more than half of the total annual precipitation, and several rainfall days contributed much of the annual precipitation, particularly at night; night-time precipitation produced 33% of the average annual precipitation on only 5% of annual rainfall days. Additionally, we found significant correlation between El Niño–Southern Oscillation (ENSO) indexes and precipitation. These findings are valuable for coping with meteorological disasters associated with extreme precipitation under global climate change. [ABSTRACT FROM AUTHOR]

As one of the largest arid and semi-arid regions in the world, central Asia (CA) is very sensitive to changes in regional climate. However, because of the poor continuity of daily observational precipitation records in CA, the spatial and temporal variations of extreme precipitation in recent decades remain unclear. Considering their good spatial and temporal continuity, gridded data, such as Climate Prediction Center (CPC) global precipitation, and reanalysis data, such as ERA-Interim (ERA), are helpful for exploring the spatial–temporal variations of extreme precipitation. This study evaluates how well CPC and ERA can represent observed precipitation extremes by comparing the differences in eight extreme precipitation indices and observation data at 84 meteorological stations. The results indicate that the CPC (except for 1979–1981) is more suitable for depicting changes in precipitation extremes. Based on the CPC data for the period 1982–2020, we found that seven indices of precipitation extremes, including consecutive wet days (CWD), max1-day precipitation amount (Rx1day), max5-day precipitation amount (Rx5day), number of heavy precipitation days (R10), very wet days (R95p), annual total precipitation in wet days (PRCPTOT), and simple precipitation intensity index (SDII) have increased by 0.2 d/10a, 0.9 mm/10a, 1.8 mm/10a, 0.3 d/10, 8.4 mm/10a, 14.3 mm/10a and 0.1 mm/d/10a, respectively, and the consecutive dry days (CDDs) have decreased by −3.10 d/10a. It is notable that CDDs decreased significantly in the north of Xinjiang (XJ) but increased in Kyrgyzstan (KG), Tajikistan (TI), and eastern Turkmenistan (TX). The other indices increased clearly in the west of XJ, north of Kazakhstan (KZ), and east of KG but decreased in the south of KG, TI, and parts of XJ. For most indices, the largest change occurred in spring, the main season of precipitation in CA. Therefore, the large-scale atmospheric circulation in April is analyzed to contrast between the most and least precipitation years for the region. A typical circulation pattern in April for those extremely wet years includes an abnormal low-pressure center at 850 hpa to the east of the Caspian Sea, which enhances the southerly winds from the Indian Ocean and hence the transportation of water vapor required for precipitation into CA. This abnormal circulation pattern occurred more frequently after 2001 than before, thus partly explaining the recent increasing trends of precipitation extremes in CA. [ABSTRACT FROM AUTHOR]

Despite having contributed the least to global warming and having the lowest emissions, the African region is the most vulnerable continent to climate change impacts. To reduce the levels of risk arising from climate change, it is mandatory to combine both mitigation and adaptation. While mitigation can reduce global warming, not all impacts can be avoided. Therefore, adaptation is essential to advance strategic interventions and reduce the impacts. As part of the international effort to cope with changing climate, a set of Coordinated Regional Downscaling Experiment (CORDEX) domains have been established worldwide. The CORDEX-Africa initiative has been developed to analyze downscaled regional climate data over the African domain for climate data analysis techniques and engage users of climate information in both sector-specific and region/space-based applications. This study takes outputs of high-resolution climate multi-models from the CORDEX-Africa initiative constructed at a spatial resolution of 50 km to assess climate change projections over Mozambique. Projected spatial and temporal changes (three 30-year time periods, the present (2011–2040), mid (2041–2070), and the end (2071–2100)) in temperature and precipitation under the Representative Concentration Pathways RCP2.6, RCP4.5, and RCP8.5 are analyzed and compared relative to the baseline period (1961–1990). Results show that there is a tendency toward an increase in annual temperature as we move toward the middle and end of the century, mainly for RCP4.5 and RCP8.5 scenarios. This is evident for the Gaza Province, north of the Tete Province, and parts of Niassa Province, where variations will be Tmax (0.92 to 4.73 °C), Tmin (1.12 to 4.85 °C), and Tmean (0.99 to 4.7 °C). In contrast, the coastal region will experience less variation (values < 0.5 °C to 3 °C). At the seasonal scale, the pattern of temperature change does not differ from that of the annual scale. The JJA and SON seasons present the largest variations in temperature compared with DJF and MAM seasons. The increase in temperature may reach 4.47 °C in DJF, 4.59 °C in MAM, 5.04 °C in JJA, and 5.25 °C in SON. Precipitation shows substantial spatial and temporal variations, both in annual and seasonal scales. The northern coastal zone region shows a reduction in precipitation, while the entire southern region, with the exception of the coastal part, shows an increase up to 40% and up to 50% in some parts of the central and northern regions, in future climates for all periods under the three reference scenarios. At the seasonal scale (DJF and MAM), the precipitation in much of Mozambique shows above average precipitation with an increase up to more than 40% under the three scenarios. In contrast, during the JJA season, the three scenarios show a decrease in precipitation. Notably, the interior part will have the largest decrease, reaching a variation of −60% over most of the Gaza, Tete, and Niassa Provinces. [ABSTRACT FROM AUTHOR]

Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981–2005) and future period (2011–2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades. [ABSTRACT FROM AUTHOR]