Future Precipitation Projections Research Articles

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

Precipitation changes during the last glacial period in the Ili Basin, northern Central Asia, as inferred from the records of loess dolomite

Understanding the climatic evolution in Central Asia (CA) and its drivers is crucial for informed decision-making and predicting global changes due to its significant contribution as a global dust source. Unfortunately, our current understanding of the pre-Holocene precipitation patterns in CA is lacking due to the limited availability of reliable proxy indicators, and our knowledge of future precipitation projections in the region, based on paleoclimatic dynamics, is also quite limited. In this study, we analyzed variations in carbonate and dolomite contents of a loess section in the Ili Basin, northern CA, to reveal precipitation changes during the last glacial period. The results showed that changes in carbonate minerals were mainly influenced by the source material supply, driven by reduced precipitation and eluviation during glacial period. We thereby established a precipitation index by removing the influence of provenance signals from the dolomite records. The index indicated lower precipitation during mid-marine isotope stage (MIS) 3 compared to MIS2, likely due to meridional shifts and intensity changes of the westerlies caused by changes in precession and obliquity, with precession playing a major role. Through the comparison of the precipitation index with the δ18O records of the Greenland ice core on a millennial timescale, it was observed that the precipitation in northern CA exhibited a positive correlation with the North Atlantic Oscillation (NAO) mode due to migration of the westerlies. By leveraging our understanding of orbital- and millennial-scale precipitation patterns, we utilized the random forest (RF) regression model and the autoregressive integrated moving average model to forecast precipitation changes for the upcoming 5000–10,000 years. The results indicated a variable pattern marked by a general upward trend, suggesting the possibility of favorable development of agricultural-based economies in the Ili River Valley. People should realize that some integrated measures are designed to improve resilience of agricultural sector in the region and enhance its capacity to adapt to challenges posed by climate change. However, more extensive research is necessary to verify these results through thorough examination and comparisons of loess sections in our research location.

Constraining Regional Hydrological Sensitivity Over Tropical Oceans

AbstractRegional hydrological sensitivity (i.e., precipitation change per degree local surface warming) contributes substantially to the uncertainty in future precipitation projections over tropical oceans. Here, we investigate the sensitivity of relative precipitation (P*, precipitation divided by the basin average precipitation) to local sea surface temperature (SST) change by dissecting it into three components, namely the sensitivity of P* to relative SST (SSTrel, SST minus the tropical mean SST) changes, the sensitivity of P* to surface convergence changes, and the sensitivity of surface convergence to SST gradient changes. We show that the relationships between P* and SSTrel, and between P*, surface convergence, and SST gradients are largely constant during climate change. This allows us to constrain regional hydrological sensitivity based on present‐day SST‐precipitation relationships. The sensitivity of surface convergence to SST gradient changes is a main source of uncertainty in regional hydrological sensitivity and is likely underestimated in GCMs.

An ensemble approach to downscale climatological variables via screening of clustered ANNs

ABSTRACT The statistical downscaling of global circulation models presents a significant challenge in selecting appropriate input variables from a vast pool of predictors. To address the issue, we developed ensemble approach based on the Combining Multiple Clusters via Similarity Graph (COMUSA), which integrates k-means and self-organizing maps (SOMs) methods with the mutual information (MI)-random sampling approach. This innovative feature extraction technique demonstrated a 21% improvement in the classification efficacy of large-scale climatic variables. When comparing feature extraction methods, the combination of MI-random sampling and ensemble clustering yielded more accurate results than SOM clustering alone. The most efficient artificial neural networks (ANNs)-based downscaling model was employed to project near- and mid-future precipitation and temperature (2025–2035, 2035–2045), revealing varied outcomes under different scenarios (SSP3-7.0 and SSP5-8.5). Under SSP3-7.0 and SSP5-8.5, annual mean precipitation values are projected to decrease by 2–3 and by 4–5%. Also, projected annual mean temperature values indicate an increase in 21-27 and 29-35% under SSP3-7.0 and SSP5-8.5 scenarios. Integrating COMUSA ensemble clustering with MI-random sampling enhances the estimation accuracy of the ANN downscaling model, contributing to accurate projections of future precipitation and temperature values.

Identifying urban prone areas to flash floods: The case of Santa Cruz de Tenerife

Floods are the natural hazard that causes the largest annual losses in the world Urban expansion and population growth have made cities the most hazardous areas, mainly due to poor planning, occupation of the drainage network and soil sealing. Santa Cruz de Tenerife is one of the many cities worldwide threatened by this phenomenon. Its typically Mediterranean rainfall pattern, characterized by extreme precipitation events in short periods of time, together with its orography of steep ravines, short length and width, as well as its disorderly growth, make it a space prone to the occurrence of flash floods. In addition, the increase in torrential rainfall as a consequence of climate change and the tendency towards greater irregularity in precipitation is considerably intensifying the problem. This paper studies the characteristics of these episodes and the black spots inventoried in its General Management Plan (PGO for its initials in Spanish). On the other hand, flood modeling is carried out based on the rainfall characterization, whose maximum flows, in total, range between 500 and 1600 m3/s. Finally, a methodology that allows integrating both analyses to obtain a detailed hazard map is proposed as an alternative to the more traditional flood hazard analyses. A design storm of 288 mm is applied and the data is validated against the largest rainfall event on record, March 31, 2002. It has been shown that in an urban drainage network, the main watercourses and those that have disappeared due to urbanization represent areas susceptible to flooding and are the sectors that should be emphasized during the implementation of risk reduction measures. Finally, emphasis is placed on the need to integrate future climate projections of precipitation to better define the maximum flood flows.

Future projection of extreme precipitation using a pseudo-global warming method: A case study of the 2013 Alberta flooding event

The June 2013 extreme precipitation event in Alberta resulted in devastating flash floods that caused significant economic losses and societal disruption. In this study, two high-resolution experiments were conducted using the Weather Research and Forecasting (WRF) model to study the change of the 2013 Alberta extreme precipitation event in a warmer climate. The control experiment was forced with 6-hourly ERA-Interim reanalysis data, while the sensitivity experiment was forced with perturbed ERA-Interim reanalysis data with climate change signals derived from ten global climate models under the Representative Concentration Pathway 8.5 emission scenario. The results indicate that the 2013 Alberta extreme precipitation event is projected to exhibit two significant characteristics in a warming climate. First, precipitation is expected to increase over the Canadian Rocky Mountain region and eastern British Columbia. Second, the precipitation is expected to decrease over the Alberta and Saskatchewan Prairies. Future changes in the extreme precipitation event are associated with changes in the cyclone evolution, moisture transport, and atmospheric stability change caused by climate change. We also found that the increase in atmospheric stability due to the decrease of relative humidity in the lower atmosphere cause less precipitation to form over the plains and later enhance the orographic precipitation in the Canadian Rockies. In addition to the general increase of precipitable water under global warming, this mechanism causes the storm's precipitation to be more concentrated near the Canadian Rockies. The findings from this study could be beneficial for understanding future changes in extreme precipitation events that share similar characteristics.

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

The combined impact of climate change scenarios and land use changes on water resources in a semi-arid watershed

This study proposes a combined approach involving climate change scenarios and varied land use and land cover (LULC) projections in a semi-arid watershed (El Grou) located in Morocco. The Soil and Water Assessment Tool (SWAT) model was coupled with land use scenarios generated using the Cellular Automata-Markov model for three dates (2030, 2040, and 2050) and future climate projections from the CMIP5 model under RCP 4.5 and 8.5 scenarios. Future precipitation and temperature projections indicate a decrease in annual precipitation by 5 % to 34 % and an increase in average temperatures, particularly during the summer months. LULC changes reveal a significant decline in agricultural lands and forests, with an expansion of bare soil by 2050. These changes, analyzed through three SWAT models representing different time periods, indicate a decrease in surface runoff by 41 % to 73 %, a reduction in total water yield by 21 % to 53 %, and a decline in groundwater recharge, posing significant challenges for water resource management and ecosystem health. The study underscores the need for integrated land and water management strategies, incorporating climate change adaptation measures, to ensure sustainable water resource utilization and build resilience in the El Grou watershed and similar semi-arid regions.

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints

Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.

The difference in the uncertainty sources between future projections of mean and extreme precipitation over East Asia

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.

Statistical downscaling of future temperature and precipitation projections in Iraq under climate change scenarios

The research was aimed to; (i) study the observed climate in Iraq from 1971 to 2021 for annually, monthly and seasonally averages of temperature and precipitation for nine meteorological stations; ii) produce RCP4.5 and RCP8.5 scenarios for Iraq region under historical (1900–2005) and future periods (2006–2100) for temperature and precipitation using NCAR-CCSM4 database; and iii) examine the anomaly for near future (2025–2034) and medium future (2050–2059), related to 1986–2005. To evaluate the performance of the monthly averages of temperature and precipitation, three bias correction techniques i.e., Delta, Empirical Quantile Mapping, and Quantile Mapping were applied to nine metrological stations. The results revealed that the Delta is the most efficient method to bias correction for the monthly averages related to temperature and precipitation. The annual average temperature increased from 22.9 C° in 2006, based on both RCP4.5 and RCP8.5, to 24.0 C° and 27.2 C°, respectively, and the anomaly increased from −0.9 C° and −1.9 C° in 2006, to 0.6 C° and 2.4 C°. The precipitation annual and winter month's averages under RCP4.5 and RCP8.5 from 2006 to 2100, 2020–2039 and 2040–2059 showed that southwest region in Iraq (Basra, Missan and Thi-Qar provinces) had more drought and low precipitation than the other regions. From 2029 to 2034 and 2050–2054, rainy years have been predicted, using RCP4.5. Based on RCP.8.5, from 2025 to 2034 and 2050–2055, should witness rainy years, followed by a drought period. The results expected to support decision-makers in incorporating a national initiative to break down social barriers, encourage growth prospects, particularly in southern Iraq.

A Method to Assess and Explain Changes in Sub‐Daily Precipitation Return Levels From Convection‐Permitting Simulations

AbstractReliable projections of extreme future precipitation are fundamental for risk management and adaptation strategies. Convection‐permitting models (CPMs) explicitly resolve large convective systems and represent sub‐daily extremes more realistically than coarser resolution models, but present short records due to the high computational costs. Here, we evaluate the potential of a non‐asymptotic approach, the Simplified Metastatistical Extreme Value (SMEV) to provide information on the future change of extreme sub‐daily return levels based on CPM simulations. We focus on a complex‐orography area in the North Eastern Italy and use three 10‐year time periods COSMO‐crCLIM simulations (2.2 km resolution) under RCP8.5 scenario. When compared to a block r‐maxima approach currently used in similar applications, the proposed approach shows reduced uncertainty in rare return level estimates (about 5%–10% smaller confidence interval) and can improve the quantification of future changes from CPM simulations. We evaluate these changes and their statistical significance in return levels for 1–24 hr durations. The changes show an interesting spatial organization associated with orography, with significant positive changes located at high elevations. These positive changes tend to increase with increasing return period and decreasing duration. Because SMEV can separate the roles of event intensity and occurrence, it allows for physical interpretations of these changes. We suggest that non‐asymptotic approaches permit the quantification of change in rare extremes within available CPM runs.

Bias Correction of Monthly Precipitation from different General Circulation Models Using Cumulative Density Function in Raipur District, Chhattisgarh, India

Accurate representation of precipitation patterns is crucial for understanding and adapting to these impacts. General Circulation Models (GCMs) are essential for projecting future climate scenarios but often exhibit biases in simulating precipitation, undermining the reliability of their outputs. This study focused on bias correction of monthly precipitation data from different GCMs using Cumulative Density Functions (CDFs). Bias correction techniques were employed to align model-simulated precipitation with observed data, revealing significant improvements in the accuracy of future precipitation projections. The study area, Raipur, characterized by diverse topography, served as the location for analysis. Three GCMs were selected based on their availability and participation in the CMIP6 experiment. The bias correction process involved the calculation of CDFs and equiprobability transformations, resulting in a closer match between model predictions and observations. Results showed substantial variability in monthly precipitation values across different climate models and scenarios, with distinct seasonal patterns observed. Inter-model discrepancies underscored the complexities of precipitation simulations, highlighting the need for careful interpretation of model outputs. Continued research efforts were crucial for improving the accuracy and reliability of climate model simulations for informed decision-making and planning in climate-sensitive sectors.

Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Storylines for Future Projections of Precipitation Over New Zealand in CMIP6 Models

AbstractLarge uncertainty exists in the sign of long‐term changes in regional scale mean precipitation across the current generation of global climate models. To explore the physical drivers of this uncertainty for New Zealand, here we adopt a storyline approach applying cluster analysis to spatial patterns of future projected seasonal mean precipitation change across CMIP6 models (n = 43). For the winter precipitation change signal, the models split roughly into two main groups: both groups have a very robust wet signal across the west coast of the South Island but differ notably in terms of the sign of precipitation change across the north of the North Island. These far north winter precipitation differences appear related to how far the Hadley cell edge and regional eddy‐driven jet shift across the models relative to their historical positions. In contrast, for summer, most models have a markedly weaker and spatially non‐uniform response, where internal variability often plays a large role. However, a small group of models predict a robust wet signal across most of the country in summer. This “wet model” group is characterized by a regional La Niña‐like increase in high pressure shifted further to the south‐east of New Zealand, associated with more frequent north‐easterly flow over the country and accompanied by significant warming of local sea surface temperatures. This regional circulation response appears related to changes in stationary Rossby wave paths as opposed to changes in La Niña occurrence frequency itself.

Significantly wetter or drier future conditions for one to two thirds of the world’s population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world’s current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century’s end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.

A Data Driven Approach for Analyzing the Effect of Climate Change on Mosquito Abundance in Europe

Mosquito-borne diseases have been spreading across Europe over the past two decades, with climate change contributing to this spread. Temperature and precipitation are key factors in a mosquito’s life cycle, and are greatly affected by climate change. Using a machine learning framework, Earth Observation data, and future climate projections of temperature and precipitation, this work studies three different cases (Veneto region in Italy, Upper Rhine Valley in Germany and Pancevo, Serbia) and focuses on (i) evaluating the impact of climate factors on mosquito abundance and (ii) long-term forecasting of mosquito abundance based on EURO-CORDEX future climate projections under different Representative Concentration Pathways (RCPs) scenarios. The study shows that increases in precipitation and temperature are directly linked to increased mosquito abundance, with temperature being the main driving factor. Additionally, as the climatic conditions become more extreme, meaning higher variance, the mosquito abundance increases. Moreover, we show that in the upcoming decades mosquito abundance is expected to increase. In the worst-case scenario (RCP8.5) Serbia will face a 10% increase, Italy around a 40% increase, and Germany will reach almost a 200% increase by 2100, relative to the decade 2010–2020. However, in terms of absolute numbers both in Italy and Germany, the expected increase is similar. An interesting finding is that either strong (RCP2.6) or moderate mitigation actions (RCP4.5) against greenhouse gas concentration lead to similar levels of future mosquito abundance, as opposed to no mitigation action at all (RCP8.5), which is projected to lead to high mosquito abundance for all cases studied.

Increased drought and extreme events over continental United States under high emissions scenario

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.