Climate Downscaling Research Articles

Assessment of Basin‐Scale Concurrent Dry and Wet Extreme Dynamics Under Multimodel CORDEX Climate Scenarios

ABSTRACTThe impact of extreme events in risk analysis depends on factors such as magnitude, duration, timing and whether the system recovers fully before the next event occurs. While previous studies have primarily examined the drivers and characteristics of individual extremes, less focus has been given to the concurrent or compounding nature of extremes across adjacent seasons. Thus, understanding the dynamics of such compound extremes, particularly dryness and wetness, is crucial. To address these concerns, a Multi Scalar Drought Index (MSDI) is formulated using precipitation and temperature data from three river basins (Brahmani, Baitarani and Cauvery) of eastern and southern India. The combinations of dryness and wetness, such as Dry‐Dry, Dry‐Wet, Wet‐Dry and Wet‐Wet, between consecutive seasons are analysed across four seasons (summer, rainy, autumn and winter). The prolonged dryness/wetness along with dry/wet year are evaluated from baseline (1979–2018) to projected COrdinated Regional Climate Downscaling Experiment (CORDEX) future period (2020–2099). The spatio‐temporal variations in intra‐annual dry‐wet extremes are identified using the Mann–Kendall test. The results suggest that the eastern Indian river basins, particularly the Brahmani and Baitarani basins, experience more frequent occurrences of compounding dryness‐wetness compared to Cauvery river which is a southern Indian basin. Future scenarios indicate a trend towards dryness during the monsoon season in Brahmani and Baitarani basins, with frequent wet extremes in late autumn and winter. Abrupt transitions between dryness and wetness are prevalent during the Rainy‐Autumn and Autumn‐Winter seasons in Brahmani and Baitarani basins. The increased frequency of compound dry‐wet extremes poses significant socio‐economic risks, including reduced agricultural productivity, water management challenges and heightened vulnerability of local livelihoods dependent on consistent water availability. The results of this study provide a scientific reference for sustainable agriculture and water resource management to predict future seasonal dry and wet alternations and develop effective mitigation strategies.

Adaptability comparison of climate downscaling methods and future climate projections in the Pearl River Delta, China

Adaptability comparison of climate downscaling methods and future climate projections in the Pearl River Delta, China

Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework

Abstract. Downscaling global climate models (GCMs) provides crucial high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g. bias, spatial pattern, annual cycle and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured, i.e. the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests of model independence, data availability and future climate change spread are conducted, resulting in a final subset of two independent models that align with our a priori expectations for downscaling over the Coordinated Regional Climate Downscaling Experiment –Southeast Asia (CORDEX-SEA).

Insights into the Australian mid-Holocene climate using downscaled climate models

Abstract. The mid-Holocene climate of Australia and the equatorial tropics of the Indonesian–Australian monsoon region is investigated using the Community Earth System Model (CESM) and the Weather Research and Forecasting (WRF) model. Each model is used to simulate the pre-industrial (1850) and the mid-Holocene (6000 years before 1950) climate. The results of these four simulations are compared to existing bioclimatic modelling of temperature and precipitation. The finer-resolution WRF simulations reduce the bias between the model and bioclimatic data results for three of the four variables available in the proxy data set. The model results show that temperatures over southern Australia at the mid-Holocene and in the pre-industrial period were similar, and temperatures were slightly warmer during the mid-Holocene over northern Australia and into the tropics compared to the pre-industrial. During the mid-Holocene, precipitation was generally reduced over northern Australia and in the Indonesian–Australian monsoon region, particularly during summertime. The results highlight the improved value of using finer-resolution models such as WRF to simulate the palaeoclimate.

Spatiotemporal functional permutation tests for comparing observed climate behavior to climate model projections

Abstract. Comparisons of observed and modeled climate behavior often focus on central tendencies, which overlook other important distributional characteristics related to quantiles and variability. We propose two permutation procedures, standard and stratified, for assessing the accuracy of climate models. Both procedures eliminate the need to model cross-correlations in the data, encouraging their application in a variety of contexts. By making only slightly stronger assumptions, the stratified procedure dramatically strengthens the ability to detect a difference in the distribution of observed and climate model data. The proposed procedures allow researchers to identify potential model deficiencies over space and time for a variety of distributional characteristics, providing a more comprehensive assessment of climate model accuracy, which will hopefully lead to further model refinements. The proposed statistical methodology is applied to temperature data generated by the state-of-the-art North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX).

Assessment of Maximum Temperature for Future Time Series over Aurangabad, Maharashtra State, India

Climatic downscaling is an important tool used by many researchers in the field of climate to downscale climatic data available for global area to study the impacts of climate on smaller area. In climatic downscaling, there are mainly two types: 1) Statistical Downscaling and 2) Dynamical Downscaling. In the present study, statistical downscaling has been performed using SDSM (Statistical Downscaling Model) to find out future values of maximum temperatures (Tmax) over Aurangabad region (Latitude: 19.8911° N, Longitude: 75.1545° E). For this downscaling, CanESM2 (Canadian Earth System Model) CMIP5 (Coupled Model Intercomparison Project Phase 5) GCM (General Circulation Model) were selected and downscaling was performed under three different RCPs (Representative Concentration Pathways): RCP 2.6, RCP 4.5 and RCP 8.5. Downscaling model is calibrated and validated successfully over the baseline period 1961-2005 and results are presented graphically as well as statistically. Future downscaling values of maximum temperatures are presented with the help of three future time series: 2020s (2011-2040), 2050s (2041-2070) and 2080s (2071-2099) and it is observed that there is increase in the values of Tmax for the future time series with respect to the baseline period. Further the heat maps are developed for increasing monthly mean daily Tmax values under three RCPs with respect to the baseline period over the duration 2006 to 2013 and compared with increased observed monthly mean daily Tmax values over the same duration. This comparison has been done to assess the pattern of increasing monthly mean daily Tmax of downscaling results over Aurangabad region. All three RCPs are showing increasing values of monthly mean daily Tmax values for the selected three future time series with respect to the baseline period. Decade wise study of downscaling results is also showing increasing values of mean monthly daily Tmax with respect to the baseline period. Tmax values are predicted to be increased under RCP 2.6 by 1.10 0C, under RCP 4.5 by 2.13 0C and under RCP 8.5 by 4.20 0C at the end of 2099 which follows the prediction given by IPCC for global level temperature rise up to 2100.

Temperature-related mortality burden and projected change in 1368 European regions: a modelling study

Excessively high and low temperatures substantially affect human health. Climate change is expected to exacerbate heat-related morbidity and mortality, presenting unprecedented challenges to public health systems. Since localised assessments of temperature-related mortality risk are essential to formulate effective public health responses and adaptation strategies, we aimed to estimate the current and future temperature-related mortality risk under four climate change scenarios across all European regions. We modelled current and future mortality due to non-optimal temperatures across 1368 European regions, considering age-specific characteristics and local socioeconomic vulnerabilities. Overseas territories were excluded from the analysis. We applied a three-stage method to estimate temperature-related risk continuously across age and spatial dimensions. Age and city-specific exposure-response functions were obtained for a comprehensive list of 854 European cities from the Urban Audit dataset of Eurostat. Regional aggregates were calculated using an aggregation and extrapolation method that incorporates the risk incidence in neighbouring cities. Mortality was projected for present conditions observed in 1991-2020 and for four different levels of global warming (1·5°C, 2°C, 3°C, and 4°C increase) by regions, and subregions using an ensemble of 11 climate models produced by the Coordinated Regional Climate Downscaling Experiment-CMIP5 over Europe, and population projection data from EUROPOP2019. Our results highlight regional disparities in temperature-related mortality across Europe. Between 1991 and 2020, the number of cold-related deaths was 2·5 times higher in eastern Europe than western Europe, and heat-related deaths were 6 times higher in southern Europe than in northern Europe. During the same time period, there were a median of 363 809 cold-related deaths (empirical 95% CI 362 493-365 310) and 43 729 heat-related deaths (39 880-45 921), with a cold-to-heat-related death ratio of 8·3:1. Under current climate policies, aligned with 3°C increase in global warming, it is estimated that temperature-related deaths could increase by 54 974 additional deaths (24 112-80 676) by 2100, driven by rising heat-related deaths and an ageing population, resulting in a cold-to-heat-related death ratio of 2·6:1. Climate change is also expected to widen disparities in regional mortality, particularly impacting southern regions of Europe as a result of a marked increase in heat-related deaths. This study shows that regional disparities in temperature-related mortality risk in Europe are substantial and will continue to increase due to the effects of climate change and an ageing population. The data presented can assist policy makers and health authorities in mitigating increasing health inequalities by prioritising the protection of more susceptible areas and older population groups. We identify the projected areas of heightened risk (southern Europe), where policy intervention aimed at building adaptation and enhancing resilience should be prioritised. European Commission.

Impacts of Climate Change on Streamflow on Dawa Sub-watershed, Genale-Dawa River Basin, Southern Ethiopia

<i>Climate change is statistical variations over an extended period in the features of the climate system, such as variations in global temperatures and precipitation, caused by human and natural sources.</i> In this study aimed to measure and examine how streamflow in the Dawa sub-basin, Genale Dawa River basin was affected by climate change. It used the average of five regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa, under two different scenarios of Representative Concentration Pathways: RCP4.5 and RCP8.5. The baseline scenario was based on the data from 1975 to 2005, while the future scenarios were based on the data from 2020s (2025–2054) and 2050s (2055–2084). The HBV hydrological model used to assess the impact on streamflow. The HBV model showed good statistical performance in simulating the impact of climate change on streamflow, with a coefficient of determination (R<sup>2</sup>) of 0.88 and Nash-Sutcliffe Efficiency (NSE) of 0.77 for monthly calibration, and R<sup>2</sup> of 0.86 and NSE of 0.83 for monthly validation. The impacts quantified using the mean monthly changes in precipitation, maximum and minimum temperatures. The bias-corrected precipitation and temperature showed a reasonable increase in both future periods for both RCP 4.5 and RCP 8.5 scenarios. These changes in climate variables resulted in a decrease in mean annual streamflow by 1.6 and 3.5% for RCP 4.5 and by 4.6 and 4.9% for RCP 8.5 scenarios of the 2020s and 2050s, respectively. Based on the analysis that predicted a drop in precipitation during the months, and seasons and an increase in precipitation during the <i>Belg</i> season, with a corresponding decrease and rise in stream flow throughout the watershed. So to offset the variation in the watershed, community should adopt various; Soil and water conservation technologies, Using drought tolerant crops, Implementing various trees and appropriate design and applying a water harvesting structure like in-situ, internal or micro catchment, external or macro catchment water harvesting and Surface runoff harvesting. This result offers useful information for current and future water resource management in the basin and similar other watershed in the country.

The University of Toronto Climate Downscaling Workflow: Tools and Resources for Climate Change Impact Analysis

The University of Toronto Climate Downscaling Workflow: Tools and Resources for Climate Change Impact Analysis

Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification

Abstract. The use of single precision in floating-point representation has become increasingly common in operational weather prediction. Meanwhile, climate simulations are still typically run in double precision. The reasons for this are likely manifold and range from concerns about compliance and conservation laws to the unknown effect of single precision on slow processes or simply the less frequent opportunity and higher computational costs of validation. Using an ensemble-based statistical methodology, Zeman and Schär (2022) could detect differences between double- and single-precision simulations from the regional weather and climate model COSMO. However, these differences are minimal and often only detectable during the first few hours or days of the simulation. To evaluate whether these differences are relevant for regional climate simulations, we have conducted 10-year-long ensemble simulations over the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) in single and double precision with 100 ensemble members. By applying the statistical testing at a grid-cell level for 47 output variables every 12 or 24 h, we only detected a marginally increased rejection rate for the single-precision climate simulations compared to the double-precision reference based on the differences in distribution for all tested variables. This increase in the rejection rate is much smaller than that arising from minor variations of the horizontal diffusion coefficient in the model. Therefore, we deem it negligible as it is masked by model uncertainty. To our knowledge, this study represents the most comprehensive analysis so far on the effects of reduced precision in a climate simulation for a realistic setting, namely with a fully fledged regional climate model in a configuration that has already been used for climate change impact and adaptation studies. The ensemble-based verification of model output at a grid-cell level and high temporal resolution is very sensitive and suitable for verifying climate models. Furthermore, the verification methodology is model-agnostic, meaning it can be applied to any model. Our findings encourage exploiting the reduction of computational costs (∼30 % for COSMO) obtained from reduced precision for regional climate simulations.

Dominant Sources of Uncertainty for Downscaled Climate: A Military Installation Perspective

AbstractWhile the Department of Defense (DoD) infrastructure is no stranger to extremes, recent events have been unprecedented, with climate change acting as a growing risk multiplier. To assess the level of exposure of DoD installations to extreme weather and climate events, site‐specific climate information is needed. One way to bridge the scale gap between outputs from existing global climate models (GCMs) and sites is climate downscaling. This makes the information more relevant for impact assessment at the DoD installation and facility scale. However, downscaling GCMs is beset by a myriad of challenges and sources of uncertainty, and downscaling methods were not designed with specific infrastructure planning and design needs in mind. Here, we evaluate state‐of‐the‐science dynamical downscaling and statistical downscaling and bias correction for climate variables (i.e., temperature and precipitation) at the daily scale. We also combine downscaling approaches in novel ways to optimize computational efficiency and reduce uncertainty. Furthermore, we examine the sensitivity of the downscaled outputs to the choice of reference data and quantify the relative uncertainty related to downscaling approach, reference data, and other factors across the climate variables and aggregation scales. Results show that empirical quantile mapping (EQM), a statistical downscaling, consistently performs well and has less sensitivity to the choice of reference data. Moreover, the hybrid downscaling that leverages EQM improves the performance of dynamical downscaling. Our findings highlight that the choice of reference data dominates uncertainties in temperature downscaling, while their role is more muted for precipitation but still non‐negligible.

Future exposure of rainfall and temperature extremes to the most populous island of Indonesia: A projection based on CORDEX simulation

AbstractThe study focuses on understanding the future exposure to rainfall and temperature extremes in one of the world's most populous islands, Java, Indonesia. We use the high‐resolution climate projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) simulation for Southeast Asia by 2100 under RCP4.5 and RCP8.5 scenarios. Results show that the island will likely experience drying in the lowlands due to annual rainfall decline by approximately 13%–18%, potentially exposing around 27%–73% of the population (varying under different scenarios) during the end of the century (2060–2099). Additionally, the future drying condition may be exacerbated by extreme temperatures with a 1.7–3.1°C increase in maximum daily temperature, linked with more than half of the population (63%) likely to experience at least an unprecedented temperature of 3°C under RCP8.5. Our seasonal analysis also suggests that dry seasons get even drier, and wet seasons get wetter. In terms of landuse areas exposed, we show a higher fraction of crop and forest areas may face both drying and warming, which can potentially lead to crop failure and wildfire. Our study indicates that compound drought and heat may be a common threat in lowland Java in the future, while intensifying rainfall extremes in the uplands may lead to flash flooding downstream and landslides. These findings highlight the urgent need for adaptation and mitigation strategies to reduce the risks associated with climate change in Java as one of Indonesia's most critical regions in the future.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (−32.2%) and SSP 4.5 (−88.8%) for 2024–2049, with further decreases expected for 2050–2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor

Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors.

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

Groundwater Depletion. Are Environmentally Friendly Energy Recharge Dams a Solution?

Groundwater is a primary source of drinking water; however, groundwater depletion constitutes a common phenomenon worldwide. The present research aims to quantify groundwater depletion in three aquifers in Greece, including the porous aquifers in the Eastern Thermaikos Gulf, Mouriki, and the Marathonas basin. The hypothesis is to reverse the phenomenon by adopting an environmentally acceptable methodology. The core of the suggested methodology was the simulation of groundwater using MODFLOW-NWT and the application of managed aquifer recharge (MAR) by using water from small dams after the generation of hydropower. Surface run-off and groundwater recharge values were obtained from the ArcSWAT simulation. The predicted future climatic data were obtained from the Coordinated Regional Climate Downscaling Experiment (CORDEX), considering the Representative Concentration Pathway (RCP) 4.5 and the climate model REMO2009. Groundwater flow simulations from 2010 to 2020 determined the existing status of the aquifers. The simulation was extended to the year 2030 to forecast the groundwater regime. In all three sites, groundwater depletion occurred in 2020, while the phenomenon will be exacerbated in 2030, as depicted in the GIS maps. During 2020, the depletion zones extended 11%, 28%, and 23% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively. During 2030, the depletion zones will increase to 50%, 42%, and 44% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively. The simulation was extended to 2040 by applying MAR with the water from the existing dams as well as from additional dams. In all sites, the application of MAR contributed to the reversal of groundwater depletion, with a significant amount of hydropower generated. Until 2040, the application of MAR will reduce the depletion zones to 0.5%, 9%, and 12% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively. Apart from over-pumping, climatic factors such as long periods of drought have exacerbated groundwater depletion. The transformation of dams to mini-scale hydropower facilities combined with MAR will benefit clean energy production, save CO2 emissions, and lead to an economically feasible strategy against groundwater depletion.

Projected Changes in Extreme Precipitation Patterns across Algerian Sub-Regions

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

The present study analyzes the projected changes of extreme climate indices over Central Asia using regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Coordinated Output for Regional Evaluations (CORE). The extreme indices are based on precipitation and temperature and are inspected for present (1981–2005) and future periods - near- (2031–2055) and far-future (2071–2095) - to assess the long-term climate change under the representative concentration pathway RCP8.5. Projected changes are analyzed for three different model ensembles. These ensembles are based on CORDEX-Central Asia (ENS_CAS, four ensemble members) and CORDEX-East Asia (ENS_EAS, six ensemble members), and a combination of both (ENS, ten ensemble members) for our study area centered over high mountain Asia, called Central East Asia (CEAS). For precipitation indices, an increase of consecutive dry days (CDD) in ENS_EAS and a slight to moderate decrease in northern parts in ENS_CAS during near-future is observed. Consecutive wet days (CWD), very heavy precipitation events (R20mm), maximum one-day precipitation (RX1day), and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain, e.g., + 7.8% / +5.6 days for CDD, + 96.6% / +0.26 days for R20mm, and + 19.7% for RX1day as median of ENS over CEAS. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for consecutive summer days (CSU, + 108.5% / +38.3 days), heat wave duration index (HWDI, + 1379.1% / +91.37 days), and the percentage of very hot days (TX90p, + 391.1% / +34.54 days). Accordingly, the number of consecutive frost days (CFD, -43.7% / -25.2 days) and the percentage of very cold days (TX10p, -83.4% / -8.13 days) are projected to decrease. The first-time usage of CORDEX-CORE and the larger ensemble size by considering simulations from overlapping domains increase the robustness of the findings from earlier studies. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountainous or lake areas. These uncertainties may be tackled by further model development with improved land-surface processes and potentially higher spatial resolution.

Climate projections of the Adriatic Sea: role of river release

The Adriatic Sea, characterized by unique local features in comparison to the broader Mediterranean Sea, stands out as a highly susceptible region to climate change. In this context, our study involves a focused climate downscaling approach, concentrating on the Adriatic water cycle. This encompasses integrated modeling at the mesoscale, covering the atmosphere, hydrology, and marine general circulation. The study period spans from 1992 to 2050, considering the high emission scenario RCP8.5. We aim at evaluating how the river release projection affects the local density stratification and the sea level rise. Indeed, the river release is found to decrease by approximately 35% in the mid-term future and condition the stratification of the water column with differences between the Northern and Southern sub-basins. The projected runoff decrease has a major impact on the Northern sub-basin, where the stratification is haline-dominated and the foreseen salinization prevails on the heating through the whole water column. Conversely, the runoff decrease has a lower impact on the Southern sub-basin, where the future changes of other mechanisms may play a major role, e.g., the changing properties of the Mediterranean water entering the Otranto Strait and the foreseen heating prevails on the salinization from the intermediate to deep water column. The study provides the first evidence of how the decreasing river discharge locally reduces the density stratification, increases the dense water, and mitigates the sea level rise in the Northern Adriatic Sea, thus acting in the opposite direction to the global warming. To minimize uncertainty in coastal ocean projections around the world, it is essential that the climate downscaling integrates high-resolution hydrology and hydrodynamics models to correctly reproduce the link between surface buoyancy and stratification and the resulting dynamics.

Empirical modeling of agricultural climate risk

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance.