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The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Research into weather circulation changes over the UK for future climate has mainly focused on changes in the Summer and Winter seasons, with less analysis on seasonality and the transition seasons. Using the 30 Met Office weather patterns we examine the influence of climate change on seasonality through atmospheric circulation using a number of climate models. Changes in seasonality are important as they can have large impacts on many sectors including agriculture, energy and tourism. This paper finds a noticeable increase in Autumn over the UK in the frequency of drier summer-type regimes and a decrease in stormy winter types that emerge as early as the 2020s. The change in circulation signal once isolated from the overall signal is responsible for a 4–12% decrease in Autumn mean rainfall on average for England by the end of this century (where the values in the range are dependent on the emissions scenario). This change is projected over English regions that are already experiencing water stress, and with predictions of drier summers over the UK in future, this could further increase drought risk. The change in circulation in Autumn also moderates the large increase in the number of large-scale extreme daily rainfall events over the same regions predicted due to climate change. While this future circulation change is replicated across all the climate models used, large differences remain in the strength of the signal between models. The climate models used replicate the frequency of the 30 weather patterns well for all seasons. [ABSTRACT FROM AUTHOR]

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa's summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize. [ABSTRACT FROM AUTHOR]

Climate change may cause adverse impact on agricultural production that could jeopardize food availability and security. In this paper, we investigate how changes in mean values and variability of weather variables may affect rice yield using panel data for 30 districts of Tamil Nadu State from 1971 to 2018. We estimate a fixed-effects regression model with panel-corrected standard errors. Results show that rainfall and temperature have a statistically significant impact on rice yield. Furthermore, weather variability, measured as standard deviations of temperature and rainfall, has a negative effect on rice yields. We use the Coupled Model Inter-comparison Project-5 outputs and dynamically downscaled the weather outputs using the regional climate model. The results of the climate model showed that the rise in temperature and disruptions in rainfall patterns including both excessive and deficient rainfall events in Tamil Nadu will continue in the future under different climate scenarios. Projected changes in the weather variables are likely to decrease rice yield in Tamil Nadu from 0.7 to 6.3% under the low emission scenario and 4.1 to 20.1% in the high emission scenario during 2022–2050 (relative to 1971–2018). These projections have implications for the planning and targeting of climate adaptation technologies such as drought-tolerant and flood-tolerant varieties to lessen the adverse impact of weather variability due to climate change in the future. [ABSTRACT FROM AUTHOR]

Spain is one of the European countries with most environmental problems related to water scarcity and droughts. Additionally, several studies suggest trends of increasing temperature and decreasing rainfall, mainly for the Iberian Peninsula, due to climate variability and change. While Regional Climate Models (RCM) are a valuable tool for understanding climate processes, the causes and plausible impacts on variables and meteorological extremes present a wide range of associated uncertainties. The multi-model ensemble approach allows the quantification and reduction of uncertainties in the predictions. The combination of models (RCM in this case), generally increases the reliability of the predictions, although there are different weighting methodologies. In this paper, a strategy is presented for the building of non-stationary PDF (probability density functions) ensembles with the aim of evaluating the spatial pattern of future risk of drought for an area. At the same time, the uncertainty associated with the metric used in the construction of the PDF ensembles is assessed. A comparative study of methodologies based on the application of the Reliability Ensemble Averaging (REA), assessing its factors using two performance measures, on the one hand the Perkins Score Methods, on the other hand the Kolmogorov-Smirnov test, is proposed. The evaluation of the sensitivity of the methodologies used in the construction of ensembles, as proposed in this paper, although without completely eliminating uncertainty, allows a better understanding of the sources and magnitude of the uncertainties involved. Despite the differences between the spatial distribution results from each metric (which can be in the order of 40 % in some areas), both approaches concluded about a plausible significant and widespread increase throughout continental Spain of the mean value of annual maximum dry spell lengths (AMDSL) between the years 1990 and 2050. Finally, the more parsimonious approach in the building of ensembles PDF, based on AMDSL in peninsular Spain, is identified. [ABSTRACT FROM AUTHOR]

This paper assesses future climate changes over East and South Asia using a regional climate model (RegCM4) with a 50 km spatial resolution. To evaluate the model performance, RegCM4 is driven with 'perfect boundary forcing' from the reanalysis data during 1970-1999 to simulate the present day climate. The model performs well in reproducing not only the mean climate and seasonality but also most of the chosen indicators of climate extremes. Future climate changes are evaluated based on two experiments driven with boundary forcing from the European-Hamburg general climate model (ECHAM5), one for the present (1970-1999) and one for the SRES A1B future scenario (2070-2099). The model predicts an annual temperature increase of about 3°-5° (smaller over the ocean and larger over the land), and an increase of annual precipitation over most of China north of 30°N and a decrease or little change in the rest of China, India and Indochina. For temperature-related extreme indicators in the future, the model predicts a generally longer growing season, more hot days in summer, and less frost days in winter. For precipitation-related extremes, the number of days with more than 10 mm of rainfall is predicted to increase north of 30°N and decrease in the south, and the maximum five-day rainfall amount and daily intensity will increase across the whole model domain. In addition, the maximum number of consecutive dry days is predicted to increase over most of the model domain, south of 40°N. Most of the Yangtze River Basin in China stands out as 'hotspots' of extreme precipitation changes, with the strongest increases of daily rain intensity, maximum five-day rain amount, and the number of consecutive dry days, suggesting increased risks of both floods and droughts. [ABSTRACT FROM AUTHOR]