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High resolution regional climate models (RCM) are necessary to capture local precipitation but are too expensive to fully explore the uncertainties associated with future projections. To resolve the large cost of RCMs, Doury et al. (2023) proposed a neural network based RCM-emulator for the near-surface temperature, at a daily and 12 km-resolution. It uses existing RCM simulations to learn the relationship between low-resolution predictors and high resolution surface variables. When trained the emulator can be applied to any low resolution simulation to produce ensembles of high resolution emulated simulations. This study assesses the suitability of applying the RCM-emulator for precipitation thanks to a novel asymmetric loss function to reproduce the entire precipitation distribution over any grid point. Under a perfect conditions framework, the resulting emulator shows striking ability to reproduce the RCM original series with an excellent spatio-temporal correlation. In particular, a very good behaviour is obtained for the two tails of the distribution, measured by the number of dry days and the 99th quantile. Moreover, it creates consistent precipitation objects even if the highest frequency details are missed. The emulator quality holds for all simulations of the same RCM, with any driving GCM, ensuring transferability of the tool to GCMs never downscaled by the RCM. A first showcase of downscaling GCM simulations showed that the RCM-emulator brings significant added-value with respect to the GCM as it produces the correct high resolution spatial structure and heavy precipitation intensity. Nevertheless, further work is needed to establish a relevant evaluation framework for GCM applications. [ABSTRACT FROM AUTHOR]

This study investigates the possible effects of climate change on temperature and precipitation variables in the Eastern Black Sea Basin, Türkiye's wettest and flood-prone region. The outputs of three GCMs under historical, RCP4.5, and RCP8.5 scenarios were downscaled to regional scale using the multivariate adaptive regression splines method. The future monthly temperature and precipitation for 12 stations in the basin were projected for three periods: the 2030s (2021–2050), 2060s (2051–2080), and 2090s (2081–2100). In addition to relative changes, high and low groups and intra-period trends were analyzed for the first time using innovative methods. For the pessimistic scenario, an increase of 3.5 °C in the interior and 3.0 °C in the coastal areas of the basin is projected. For the optimistic scenario, these values are expected to be 2.5 and 2.0 °C, respectively. A decrease in precipitation is projected for the interior region, and a significant increase is expected for the eastern and coastal areas of the basin, especially in spring. This result indicates that floods will occur frequently coastal areas of the basin in the coming periods. Also, although the monotonic trends of temperatures during periods are higher than precipitation in interior regions, these regions may have more uncertainty as their trends are in different directions of low and high groups of different scenarios and GCMs and contribute to all trends, especially precipitation. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of obtaining detailed wind speed data in complex terrains by employing dynamic and statistical downscaling techniques. The research utilizes diagnostic and prognostic dynamic models, along with the Decaying Average (DA) and Analog Ensemble (AE) methods, starting from ERA5 global reanalysis. The Weather Research and Forecasting (WRF) and California Meteorological (CALMET) models are evaluated for their performance in simulating 10 m wind speed in the Zhangjiakou competition zone during the Beijing 2022 Winter Olympics. The study compares the accuracy of various downscaling approaches for wind speed estimation. High-density observational data from 12 stations, covering an area of about 20 km² and collected between October and April from 2017 to 2022, were employed for model calibration and validation. The research reveals that the planetary boundary layer scheme and land surface scheme significantly impact the WRF model's wind speed simulation. Meanwhile, the slope flow parameter emerges as a key sensitivity factor for the CALMET model. Despite inheriting biases from ERA5 and WRF, the CALMET model, driven by ERA5 and WRF at a 100 m resolution, offers a reasonable spatial distribution of wind speed which was required by the ski competition. Both the DA and AE methods prove effective in correcting biases in 10 m wind speed from the ERA5, WRF, and CALMET models, with the AE method generally outperforming the DA method. The AE method reduces the root mean square error of the CALMET model's 10 m wind speed from 3.1 m/s to 1.5 m/s at locations with stations. In conclusion, the study identifies the combination of the CALMET model and the AE bias-corrected method as the optimal dynamic-statistical downscaling scheme for 10 m wind speed in the study area. [ABSTRACT FROM AUTHOR]

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation. Plain Language Summary: Natural hazards, such as flooding and extreme heat, can damage our infrastructure and local communities. These hazards often stem from extreme weather conditions such as heavy precipitation and hot temperatures. Global climate change is expected to affect precipitation and air temperature, leading to more extreme weather events and hazards in the future. Predicting frequency and severity of these future events is key to protect our infrastructure and communities. Climate models have been widely used to project statistics of future extremes. However, they come with uncertainties due to numerical approximations, low spatial resolutions etc. Consequently, predictions generated by these models are inherently uncertain. This paper used a new approach to improve future projections of extreme climatic events, focusing on South Florida. Our findings revealed substantial improvements in projecting future events, particularly for extreme precipitation. We also showed that nationally and globally derived simulations may not be suitable for accurately projecting heavy precipitation, but can suffice for air temperature and low/medium precipitation rates. This paper offers promising avenues for refining projections of future weather events. By enhancing our ability to anticipate upcoming weather events more accurately, we can better protect our infrastructure and communities against the challenges posed by climate change. Key Points: A hybrid statistical approach to improve bias correction of GCM precipitation and air temperature simulationsMajor improvements in both climatic variables, especially extreme precipitation, using our hybrid statistical approach in South FloridaCoarse resolution downscaled data may be insufficient for deriving high quantile precipitation at the regional scale [ABSTRACT FROM AUTHOR]

[Objective] Understanding and grasping the temporal and spatial changes in meteorology and reasonably predicting the future trend of climate change can provide an important theoretical basis for the formulation of regional response measures. [Methods] The Sichuan section of the Yangtze River Basin was selected as the research area. Data analysis was conducted based on measured station data from 1980 to 2014. Three future hypothetical climate scenario models (SSP1-2.6, SSP2-4.5, SSP5-8.5) were estimated using the GCM with complete scenarios in the sixth phase of the International Coupled Model Comparison Plan (CMIP6) after bias correction, and the changes in precipitation climate variables within the region were analyzed. [Results (1) There is a certain degree of underestimation of precipitation simulation in climate models, with monthly scale seasonal scale annual scale in terms of time scale. (2) After the calibration and evaluation of the climate model, the data are more consistent with the actual situation, and the quantile mapping method is very effective for the calibration of the Climate model data. (3) Compared to the benchmark period (1980-2014), the future precipitation in the study area is higher than the historical period under three scenarios, with SSP5-8.5 SSP2-4.5 SSP1-2.6. (4) The areas with significant increases in precipitation are mainly located in the northwest of the study area, showing a spatial pattern of low level in the west and high level in the east. [Conclusion] The climate model's prediction of future precipitation changes in the study area shows an overall increasing trend, but there is underestimation to a certain extent. [ABSTRACT FROM AUTHOR]

Recognizing the differential impacts of climate change across geographical scales, this study emphasizes the importance of statistical downscaling. Using Gene Expression Programming (GEP) and Linear Genetic Programming (LGP), statistical downscaling transforms broad climate trends into region-specific insights. This allowed for detailed analyses of anticipated changes in sediment yield and discharge within a Euphrates River sub-basin in Türkiye using large-scale variables from the CanESM2 model. The dataset is divided into calibration (1970–1995) and validation (1996–2005) periods. To assess the models’ accuracy, statistical measures such as RMSE, MAE, NSE, and R were used. The analysis revealed that LGP outperformed GEP in both discharge and sediment yield during validation, with RMSE = 51.79 m3/s and 4,325.66 tons/day, MAE = 27.14 m3/s and 1,593.34 tons/day, NSE = 0.684 and 0.627, and R = 0.841 and 0.788, respectively. However, when simulating future periods based on the observed period (2006–2020), the GEP model was superior to LGP under RCP2.6, RCP4.5, and RCP 8.5 scenarios from CanESM2. In 2021–2100, models suggest a moderate decrease in discharge and sediment yield, indicating potential shifts in the basin's hydrodynamics. These changes could disrupt hydropower generation, challenge water management practices, and alter riverine ecosystems. The results necessitate a thorough assessment of potential ecological consequences. HIGHLIGHTS Climate change effects on streamflow and sediment yield have been explored.; Genetic programming such as GEP and LGP have been employed in statistical downscaling technique.; Future projection of streamflow and sediment yield have been done under different emission scenarios.;

This study examines two downscaling techniques, convolutional neural networks (CNNs) and feedforward neural networks for predicting precipitation and temperature, alongside statistical downscaling as a benchmark model. The daily climate predictors were extracted from the European Center for Medium-range Weather Forecast (ECMWF) ERA5 dataset spanning from 1979 to 2010 for Tabriz city, located in the northwest of Iran. The biases in precipitation data of ERA5 predictors were corrected through the empirical quantile mapping method. Also, two nonlinear predictor screening methods, random forest and mutual information, were employed, alongside linear correlation coefficient. While these methods facilitate identification of dominant regional climate change drivers, it is essential to consider their limitations, such as sensitivity to parameter settings, assumptions about data relationships, potential biases in handling redundancy and correlation, challenges in generalizability across datasets, and computational complexity. Evaluation results indicated that CNN, when applied without predictor screening, achieves coefficient of determination of 0.98 for temperature and 0.71 for precipitation. Ultimately, future projections were employed under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and concluded that the most increase in temperature by 2.9 °C and decrease in precipitation by 3.5 mm may occur under SSP5-8.5. HIGHLIGHTS Convolutional neural networks (CNNs) and feedforward neural networks (FFNNs) were used for downscaling general circulation model.; The empirical quantile mapping method was used for bias correction.; Future projections were employed under two shared socioeconomic pathways (SSPs), i.e., SSP2-4.5 and SSP5-8.5.; The results show the superiority of CNN over other AI methods.;

Argentina is a country with a variety of climates, where an increase in mean and extreme temperatures is currently on-going, demanding regional climate information to design and implement effective strategies for climate change adaptation. In this regard, the use of empirical statistical downscaling (ESD) procedures can help provide tailored climate information. In this work, a set of ESD models were tested and applied to generate plausible regional climate projections for daily maximum and minimum temperatures (Tx, Tn) in Argentina. ESD models were applied to an ensemble of CMIP5 and CMIP6 global circulation models (GCMs) to downscale historical and future RCP8.5 and SSP585 scenarios. The plausibility of the ESD projections was analysed by comparing them with their driving GCMs and with CORDEX regional climate models (RCMs). Generally, all ESD models added value during the historical period, in mean values as well as in extreme indices, especially for Tx. The climate projections depicted an extended signal of warming (both in the mean and in the frequency of extremes), consistent between all simulations (GCMs, RCMs and ESD) and strongest over northern Argentina. ESD models showed potential to produce plausible projections, although, depending on the technique considered (for Tx) and the predictor configurations (for Tn), differences in the change rates were identified. Nevertheless, the uncertainty in future changes was considerably reduced by RCMs and ESD when compared to their driving GCMs. Overall, this study evidences the potential of ESD in a climate change context and contributes to the assessment of the uncertainty on the future Argentine climate. [ABSTRACT FROM AUTHOR]

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]

The Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau. Plain Language Summary: The Three‐River Headwater Region, which is also known as the Sanjiangyuan in Chinese, is located in Qinghai Tibetan Plateau, China. It is considered the water tower of China because it is the source of three giant rivers the Yangtze, Yellow, and Lancang (Mekong). However, its water resources (hydrological cycle) are very sensitive and vulnerable to changing climate. Therefore, we assessed the potential impact of climate change on all the important hydrological components such as precipitation, streamflow, snow melt flow, surface flow, baseflow, soil moisture (SM), and changes in terrestrial water storage. Previous studies mainly focused on precipitation, streamflow, and SM. Global Climate Models (GCMs) are the main tool to assess the future changes in hydrological components under changing climate. Since GCMs have a coarse spatial resolution and biases in their outputs, a statistical downscaling model (SDSM) was applied to fix these issues and used to generate climate data (e.g., temperature and precipitation) for the future (2021–2100) under two scenarios (i.e., SSP2‐4.5 and SSP5‐8.5). These scenarios represent the global development and greenhouse gas emissions in the future. SSP2‐4.5 scenario typically involves moderate greenhouse gas emissions reduction efforts and some adaptation and mitigation measures to address climate change impacts, and SSP5‐8.5 represents high greenhouse gas emissions and limited efforts to mitigate climate change impacts. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The downscaled future climate data was used as input to run a hydrological model (HEC‐HMS) to generate hydrological components under both scenarios. The future changes in the hydrological components were obtained for 2021–2060 and 2061–2100 with respect to the baseline period 1981–2020. The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. Almost all components are expected to increase in the future under both scenarios. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the second half of the twenty‐first century (2061–2100). Peak flows are expected to be much higher than the present conditions and to shift forward. Snowmelt will start earlier in the future. This study will be very useful in understanding the hydrological cycle and can be used by policymakers, planners, and stakeholders for proactive adaptation strategies such as water resources planning and management, investments in water infrastructure, land use planning, ecosystem restoration, and community resilience‐building initiatives to mitigate potential risks. Key Points: The region will be hotter and wetter, with intensive and frequent floodsThe hydrological components are expected to increase in the futureSurface flow showed an unprecedented increase of 500%–1,000% [ABSTRACT FROM AUTHOR]

Reliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parametrization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU‐Net) for the maximum and minimum air temperature (Tmax and Tmin respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 to 1.5 km over South Korea through the fusion of deep learning (i.e., U‐Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU‐Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression‐based statistical downscaling model at the seen and unseen stations for forecasting the next‐day Tmax and Tmin, DeU‐Net showed the highest spatial correlation and the lowest root‐mean‐square error in all cases. In a qualitative evaluation, DeU‐Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to 7 days indicated that the proposed DeU‐Net is a better downscaling approach than support vector regression, regardless of the forecast lead time. These results demonstrate that bias‐corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting. [ABSTRACT FROM AUTHOR]

We analyzed hydrological responses to changing climate and land use/land cover (LULC) for the past (1985–2020) and future (2021–2080) in the Chemoga watershed of the Upper Blue Nile Basin. The watershed comprises four agroecological environments: Moist Kolla, Moist Weyna Dega, Moist Dega, and Wet Wurch. Past and projected LULC changes under business-as-usual (BAU) and land conservation (LC) scenarios were utilized. Climate projections from 2021 to 2080, under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), were downscaled from Global Climate Models. Utilizing the Soil and Water Analysis Tool, we assessed impacts on mean annual surface runoff (SR) and evapotranspiration (ET). Maximum and minimum temperatures increased significantly in the past and future climate scenarios, with a significant rainfall increase observed under SSP5-8.5. Historical trends revealed a 16.6% increase in SR and 7% in ET from 1983–2002 to 2003–2020. Under BAU LULC with the SSP2-4.5 (SSP5-8.5) climate scenario, SR increased by 24% (26.1%) and ET by 3.1% (4.4%) from 2003–2020 to 2021–2050, followed by a subsequent SR rise of 13.7% (14.0%) and ET increase of 6.0% (5.7%) from 2021–2050 to 2051–2080. Conversely, the LC LULC with SSP2-4.5 (SSP5-8.5) resulted in a 5.3% (4.2%) SR decrease and ET increase of 9.7% (11.3%) from 2003–2020 to 2021–2050 and a further SR decrease of 1% (0.7%) and 6.1% (6.9%) ET increase from 2021–2050 to 2051–2080. The Moist Kolla agroecology experienced the highest SR increase due to vegetation clearances for commercial farming. Meanwhile, the LC scenario indicated substantial decreases in SR and marginal increases in ET in the Moist Weyna Dega agroecology due to forest restoration on steep slopes. Overall, SR showed greater sensitivity to LULC changes, while ET was more responsive to climate changes. The results emphasize considering diverse agroecological contexts for effective water resource management under changing climate and LULC scenarios. [ABSTRACT FROM AUTHOR]

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Abstract Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

High-resolution bioclimatic data are crucial to providing fine-scaled insights into biodiversity assessment, forestry, and agricultural management. Existing global bioclimatic datasets often exhibit kilometer-level coarse resolution or have miss the data in recent decades, potentially resulting in the issues of lower spatial accuracy, limited information, and restricted applicability in fine-scaled studies. Hubei Province in Yangtze River Middle Reaches has sparse meteorological stations in high-altitude mountainous areas to map the high-resolution bioclimatic variables directly. This study developed a 30-year averaged bioclimatic dataset for Hubei Province during 1991–2020 at a 30-m spatial resolution, utilizing monthly temperatures and precipitation derived from a downscaling-calibration framework. The downscaling of 1-km resolution climate variables was achieved by using a random forest model with 30-m resolution terrain and spatial covariates. Then, the geographical differential analysis was applied to improve the accuracy of downscaled products by including additional ground data. The mean absolute errors of calibrated monthly maximum, mean, minimum temperatures, and precipitation based on ordinary kriging decreased from 0.74 °C, 0.47 °C, 0.47 °C, and 28.27 mm to 0.43 °C, 0.28 °C, 0.36 °C, and 21.43 mm, respectively. Finally, calibrated climate variables were employed to calculate 19 annual bioclimatic variables, which were subsequently averaged over the 30-year period. The constructed bioclimatic dataset exhibits high overall consistency with the WorldClim dataset according to pixel-based comparison (Spearman correlation coefficients >0.6), with differences mainly attributed to the superior local accuracy of our dataset and climate changes. The dataset will provide fine-scaled, updated, and reliable data supports for local-related studies and decision making.

Expanding the South American renewable energy matrix to ensure more sustainable socio-economic development, mitigate the climate change effects, and meet the targets set in the Paris Agreement is crucial. Hence, this study sought to estimate South America's wind speed and wind power density alterations projected by eight global climate models (GCMs) from the Coupled Model Intercomparison Project—Phase 6 (CMIP6). To this end, we applied statistical downscaling and bias correction to the GCMs outputs through the Quantile Delta Mapping method and assessed the projected changes in wind power in future climate under the Shared Socioeconomic Pathways (SSPs) SSP2-4.5 and SSP5-8.5 emission scenarios. ERA5 reanalysis data from 1995 to 2014 validated the models' historical simulations. The CMIP6 multi-model ensemble indicated an approximate 25–50% increase in wind power density in sectors such as Northeast and South Brazil and growing wind power in regions such as Argentine Patagonia, northern Venezuela, and portions of Uruguay, Bolivia, and Paraguay. Estimates of the wind power growth for the twenty-first century in those regions reiterated their potential performance in the historical period. For the SSP5-8.5 emission scenario, the ensemble projections indicated even more favorable wind power conditions in the sectors mentioned. However, individual projections of wind intensity anomalies obtained by each ensemble member showed a large spread among the GCMs, evidencing the uncertainties associated with the prospects of change in wind power on the continent. Furthermore, this study has presented a first analysis of CMIP6 projections for South American wind power generation, providing relevant information to the energy sector decision-makers. [ABSTRACT FROM AUTHOR]

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While there has been rapid advancement in the development and application of statistical downscaling methods for climate projection, determining the best predictive large‐scale climate information for the targeted local climate variable remains a challenge. The choice of predictor variables is one of the most influential steps of model development and has the potential to lead to varying results, contributing to the total uncertainty of future projections. Despite being a well‐known problem, predictor selection often does not receive adequate attention and the development of a straightforward and feasible prescreening process is needed to provide guidance to simple (e.g., linear regression) and complex (e.g., machine learning) modeling tools alike. In this project, Akaike information criterion (AIC) and leave one out cross validation are used to evaluate sets of predictor variables common in statistical downscaling models of precipitation (e.g., temperature, geopotential height, moisture transport). Because thousands of predictor sets were found to be competitive in their statistical skill in projecting rainfall, results suggest that an ensemble of predictor sets should be used to account for the resulting variance associated with predictor selection. This ensemble approach is applied to make improvements to the most recent statistical downscaling model developed for rainfall projection in Hawai'i. An ensemble validation study is performed by projecting future wet season rainfall in Hawai'i for scenario RCP4.5 using 17 CMIP5 GCMs with ensembles of highly ranked and poorly ranked predictor sets. Results show significantly less variance and improved agreement in the projected sign of precipitation change for the highly ranked predictor set ensemble. In conclusion, it is recommended that statistical downscaling models are run in an ensemble‐mode with multiple combinations of predictors instead of using a single model, allowing for additional quantification of model uncertainty and a "safety net" for when the true physically best‐suited predictor set is unknown. [ABSTRACT FROM AUTHOR]

Nowadays, meteorological data plays a crucial role in various fields such as remote sensing, weather forecasting, climate change, and agriculture. The regional and local studies call for high spatial resolution gridded meteorological data to identify refined details, which however is generally limited due to the models, platforms, sensors, etc. Downscaling has been a significant and practical way to improve spatial resolution. In recent years, with superior feature extraction and expression abilities, deep learning (DL) has outperformed traditional methods in various areas, and exhibits huge potential to establish a complicated mapping between large-scale and local-scale meteorological data. Therefore, this paper provides a systematic review of DL in statistical downscaling for deriving high spatial resolution gridded meteorological data. This review first presents the background, including a taxonomy of downscaling methods, the role of DL in statistical downscaling, and the analogy between downscaling and image super-resolution. It shows evidence of how downscaling can benefit from DL, particularly super-resolution networks. Subsequently, this review focuses on the recent development of the DL-based statistical downscaling of gridded meteorological data, especially the deep architectures, including convolutional neural networks to capture the spatial dependencies of meteorological variables, recurrent neural networks to reveal the temporal states from time series, and generative adversarial networks to facilitate the reconstruction of high-frequency details, as well as the major structure residual learning and attention mechanism. In addition, this review demonstrates the specific issues towards downscaling, including scaling factors, spatial–temporal and variable correlations, and paired datasets construction, and then gives a comprehensive summary of the status of datasets, toolsets and metrics. The future challenges from the perspective of unsupervised models, transformer architecture, data fusion, physical-informed learning, generalization capacity, and uncertainty quantification for downscaling are finally discussed. • Deep learning maps large-scale predictors to local-scale predictand for downscaling. • The major deep structures for downscaling networks and enhancement are reviewed. • The specific issues on downscaling factor, correlations, and datasets are addressed. • Unsupervised, physical-informed learning, uncertainty etc. pose great challenges. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have become one of the state‐of‐the‐art techniques for downscaling climate projections. They are being applied under Perfect‐Prognosis (trained in a historical period with observations) and hybrid approaches (as Regional Climate Models (RCMs) emulators), with satisfactory results. Nevertheless, two important aspects have not been, to our knowledge, properly assessed yet: (1) their performance as emulators for other Earth System Models (ESMs) different to the one used for training, and (2) their performance under extrapolation, that is, when applied outside of their calibration range. In this study, we use UNET, a popular CNN, to assess these two aspects through two pseudo‐reality experiments, and we compare it with simpler emulators: an interpolation and a linear regression. The RCA4 regional model, with 0.11° resolution over a complex domain centered in the Pyrenees, and driven by the CNRM‐CM5 global model is used to train the emulators. Two frameworks are followed for the training: predictors are taken (1) from the upscaled RCM and (2) from the ESM. In both frameworks, the performance of the UNET when applied for other ESMs different to the one used for training is considerably worse, indicating poor generalization. For the linear method a similar deterioration is seen, so this limitation does not seem method specific but inherent to the task. For the second experiment, the emulators are trained in present and evaluated in future, under extrapolation. While averaged aspects such as the mean values are well simulated in future, significant biases (up to 5°C) appear when assessing warm extremes. These biases are larger by UNET than those produced by the linear method. This limitation suggests that, for variables such as temperature, with a marked signal of change and a strong linear relationship with predictors, simple linear methods might be more appropriate than the sophisticated deep learning techniques. [ABSTRACT FROM AUTHOR]

Climatic statistical downscaling in arid and topographically complex river basins remains relatively lacking. To address this gap, climatic variables derived from a global climate model (GCM) ensemble were downscaled from a grid resolution of 2.5° × 2.5° down to the station level. For this purpose, a combination of multiple linear and logistic regressions was developed, calibrated and validated with regard to their predictions of monthly precipitation and daily temperature in the Jordan River Basin. Seasonal standardized predictors were selected using a backward stepwise regression. The validated models were used to examine future scenarios based on GCM simulations under two Representative Concentration Pathways (RCP4.5 and RCP8.5) for the period 2006–2050. The results showed a cumulative near-surface air temperature increase of 1.54 °C and 2.11 °C and a cumulative precipitation decrease of 100 mm and 135 mm under the RCP4.5 and RCP8.5, respectively, by 2050. This pattern will inevitably add stress to water resources, increasing management challenges in the semi-arid to arid regions of the basin. Moreover, the current application highlights the potential of adopting regression-based models to downscale GCM predictions and inform future water resources management in poorly monitored arid regions at the river basin scale. [ABSTRACT FROM AUTHOR]

Global Climate Models (GCMs) depict a notable influence of climate change on southern South America (SSA). Future regional-to-local information for adaptation and mitigation policies can be obtained by downscaling over GCMs outputs, increasing the resolution of the climate projections. Current statistical downscaling approaches in the region [e.g., Generalised Linear Models (GLMs)] need to undergo "human-guided" feature selection, which is one of the main sources of uncertainty. Here, we explore the advantages and limitations of using Convolutional Neural Networks (CNNs) in SSA to downscale daily minimum and maximum temperatures. For this purpose, we elaborate three different experiments: a cross-validation (CV) in the present climate; downscaling the historical and RCP8.5 scenarios of the EC-Earth; a pseudo-reality experiment to measure the extrapolation skill. CV-experiment results show no remarkable differences between CNNs and GLMs, although the non-linearity of the CNNs improved the representation of the extreme aspects of temperatures. Additionally, we use eXplainable Artificial Intelligence to prove that co-linearities are better handled in CNNs. The pseudo-reality experiment shows a good extrapolation skill of CNNs, especially when the activation functions are linear. Overall, the automatic skill of CNNs to deal with co-linearities in predictor data—against conventional approaches—together with the plausible climate change projections obtained—verified with the pseudo-reality experiment—make them attractive to be used for downscaling beyond their non-linear nature. These results enforce the idea of incorporating CNNs into the battery of downscaling techniques over SSA and provide experimental guidelines with prospects to be utilised in climate change studies. [ABSTRACT FROM AUTHOR]

Climate change is a global challenge necessitating adaptation at the local level. Small island developing states (SIDS) in the southwest Indian Ocean (SWIO) basin are particularly vulnerable and already facing significant challenges due to climate variability and extreme weather events like tropical cyclones (TCs). Tailored climate services, catering to their specific needs and contexts, are crucial for formulating appropriate adaptation strategies. This study aims to fill the gap of localized and reliable information for climate services in the SWIO region. A 12-km resolution regional climate model is dynamically downscaled from a CMIP6 model to capture the climatology of tropical cyclones. Outputs are bias-corrected at kilometer-scale resolution over multiple islands. A subset of CMIP6 simulations is also statistically downscaled over La Réunion to quantify model uncertainties at the local scale and compare statistical and dynamical downscaling methods.CMIP6 models project an average increase in daily mean temperatures over small islands ranging from 1.2 °C (SSP1-2.6) to 3.7 °C (SSP5-8.5) by the end of the century compared to the reference period of 1981–2010, and up to 4.4 °C on Madagascar (SSP5-8.5). The dry season in the SWIO region is anticipated to become significantly drier, with precipitation deficits ranging from 10 % to 40 %, primarily due to a delayed onset of the rainy season. The cyclonic risk is expected to increase due to stronger cyclone intensities, a higher proportion of strong TCs, and the poleward migration of very intense TCs by one to two degrees latitude, further amplifying the risk faced by the Mascarene islands.

Damming and flow regulation ensure water availability during dearth periods, but they cause impacts on inhabiting biota and facilitate the establishment of Invasive Alien Species (IAS). Setting environmental flows (e-flows) has proven fundamental to ensure good ecological status of rivers, battle IAS, and sustain ecosystem services. However, ongoing climate change is further straining water resources availability and the fulfilment of e-flows in Spain, which has compelled River Basin Management Plans (RBMPs) to include specific adaptation measures. In this study, we assessed adaptation strategies addressing land use and climate change in the Serpis River (Eastern Spain), based on increasing irrigation efficiency (i.e. moving from gravity to drip irrigation). The evaluation considered two CMIP6 climate change scenarios (Shared Socioeconomic Pathways SSP1-2.6 and SSP5-8.5) for two time horizons (short term 2015–2040 and mid-term 2041–2070), together with three land use change scenarios (changes in irrigated areas and technologies). Future estimations included reservoir flow releases, e-flows fulfilment, agricultural benefits, and changes in the suitable habitat and potential competition for two native (Eastern Iberian chub Squalius valentinus and European eel Anguilla anguilla) and two invasive (common bleak Alburnus alburnus and pumpkinseed Lepomis gibbosus) fish species. Predictions indicated a decrease in the suitable habitat for eel and Eastern Iberian chub, but the established e-flows will be ensured. Although the maximum benefits will rarely be achieved, on average the modernisation expected in the RBMP would sustain agricultural incomes in the optimistic scenario (SSP1-2.6) and for the short term of the pessimistic scenario (SSP5-8.5), showing similar effects than the ongoing redistribution of agricultural plots. Habitat and competition with invasive species will experience little changes due to the strong flow regulation, although the invasive bleak would prevail in the mid-term of the most pessimistic scenario. These results highlight the necessity of proactive adaptation measures beyond those described in the RBMPs (e.g. forest management or crop substitution) to increase water availability and sustain agricultural benefits, in particular for the most pessimistic scenario. They also underscore the difficulties to counteract invasive species through streamflow management under water scarcity scenarios.

Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models. HIGHLIGHTS Precipitation values are under-predicted, while temperature values are over-predicted by statistical downscaling models.; Large errors in downscaling precipitation are observed, since downscaling of precipitation is more problematic than temperature.; Statistical measures (R2, RMSE, SSE, NSE, and MAE) showed good agreement between observed and downscaled climate variables for SDSM and LS-SVM.;

Extensive investigations on the projection of heat waves (HWs) were conducted on the basis of coarse-resolution global climate models (GCMs). However, these investigations still fail to characterise the future changes in HWs regionally over China. PRECIS dynamical downscaling with a horizontal resolution of 25 km × 25 km was employed on the basis of GCM-HadCM3 to provide reliable projections on HWs over the Chinese mainland, and six statistical downscaling methods were used for bias correction under RCP4.5 and RCP8.5 scenarios. The multi-method ensemble (MME) of the top three dynamical downscaling methods with good performance was used to project future changes. Results showed that PRECIS primarily replicated the detailed spatiotemporal pattern of HWs. However, PRECIS overestimated the HWs in the Northwest and Southeast and expanded the areas of HWs in the Northeast and Southwest. Three statistical downscaling methods (quantile mapping, CDF-t and quantile delta mapping) demonstrated good performance in improving PRECIS simulation for reproducing HWs. By contrast, parametric-based trend-preserving approaches such as scaled distribution mapping and ISI-MIP are outperformed by the three aforementioned methods in downscaling HWs, particularly in the high latitudes of China. Based on MME projections, at the end of the 21st century, the national average of the number of HW days each year, the length of the longest HW event in the year and the extreme maximum temperature in HW will increase by 3 times, 1 time and 1.3 °C, respectively, under the RCP4.5 scenario, whilst that under the RCP8.5 scenario will increase by 8 times, 3 times and 3.7 °C, respectively, relative to 1986–2005. The Northwest is regionally projected to suffer long and hot HWs, whilst the South and Southeast will experience frequent consecutive HWs. Thus, HWs projected by the combined dynamical and statistical downscaling method are highly reliable in projecting HWs over China.

Climate change, particularly global warming, is a significant environmental issue that has gained widespread attention in recent decades. This study aimed to complement the model for the future by utilizing Global Climate Models (GCMs) data. The shallow-layered Artificial Neural Network (ANN) and deep-based Long Short-Term Memory (LSTM) network was applied to extract the historical temperature trend of Calgary, Canada. Mutual Information (MI) was employed for screening purposes to ensure the quality of the input variables. The results of the study indicate that the LSTM model, which relied on the data screening method using MI, achieved RMSE of 0.01 °C, DC of 0.93, a CC of 0.75 and a Bias of 1.89, and has superiority over the ANN method in the Alberta region. [ABSTRACT FROM AUTHOR]

High-resolution relative humidity (RH) data are essential in studies of climate change and in numerical meteorological forecasting. However, because high-resolution meteorological grid data require a large number of stations, the sparse distribution of ground meteorological stations in China before 2008 has limited the development of long-term and high-resolution RH products in the China Meteorological Administration's Land Assimilation System (CLDAS) dataset. To retrieve high-quality and high-resolution RH data before 2008, we propose a statistical downscaling model (SDM) based on a generative adversarial network (GAN) to transform the original RH data from a resolution of 0.05° to 0.01°. The GAN-based SDM (GSDM) is trained with the RH of the CLDAS (0.05°) dataset after 2008 as its input, and the RH of the high-resolution CLDAS (HRCLDAS, 0.01°) dataset after 2008 as its target for training. The 2-m air temperature data from the HRCLDAS dataset are also included in the input, and the station observations of RH are incorporated in the target for training. To select the optimum data combination for the model, we compared three methods: (1) incorporating without auxiliary data (GSDM), (2) incorporating air temperature as an additional input (GSDM_T), and (3) incorporating air temperature as an additional input and the RH data at stations as an additional target for training (GSDMTO). Taking the Beijing–Tianjin–Hebei region as an example, we trained the GSDM by using data from 2018 and tested the model performance in 2019. The experimental results showed that the GSDMTO algorithm achieved the lowest root-mean-square error (3.85%), followed by the GSDM_T (4.01%) and GSDM (4.95%) algorithms. The proposed models showed a competitive performance and captured more local details of the RH fields than other deep learning models and traditional bilinear interpolation. In general, the GSDM_TO algorithm using a combination of different sources of data (air temperature and observed RH) achieved the best results among the various deep learning approaches, indicating that more auxiliary data and more accurate observations are beneficial in downscaling. This may be helpful for the statistical downscaling of other meteorological data. [ABSTRACT FROM AUTHOR]

Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle–nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05° to 0.01° for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 °C/0.12 °C/0.72 °C, BIAS of −0.02 °C/0.02 °C/0.002 °C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model's feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements. [ABSTRACT FROM AUTHOR]

There are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO‐CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11° and daily data. Both the SDM and the RCMs are driven by the ERA‐Interim reanalysis, and observations are taken from the gridded dataset E‐OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long‐term trends or the sensitivity of different methods to being driven by global climate models, is needed. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional‐to‐local scales from large‐scale atmospheric fields following the perfect‐prognosis approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalize is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use‐case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on eXplainable Artificial Intelligence (XAI) techniques. Specifically, we introduce two novel XAI‐based diagnostics—Aggregated Saliency Map and Saliency Dispersion Maps—and show how they can be used to unravel the internal behavior of these models, aiding in their design and evaluation. This work advocates for the introduction of XAI techniques into deep downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions. Plain Language Summary: Due to limitations in the computational resources available, General Circulation Models (GCMs) are often used to simulate the climate system over coarse resolution grids. This hampers the applicability of GCM products in the regional‐to‐local scale, highly demanded by different socio‐economic sectors. Statistical downscaling aims to solve this problem by generating high‐resolution climate fields. Recently, machine learning techniques—particularly deep learning (DL) models—have shown promising results in this task. These models are first trained in a historical period through observational data sets, and then applied to the GCM outputs of plausible future scenarios, thus generating high‐resolution climate change products. To assess the performance of these methods, a number of evaluation metrics have been proposed considering both the skill to reproduce present climate conditions and the ability to generalize changing conditions. Here, we illustrate the possibilities of eXplainable Artificial Intelligence (XAI) techniques to expand the evaluation framework for deep downscaling methods, introducing new XAI‐derived diagnostics to unravel their internal behavior. The results show the usefulness of incorporating XAI techniques into statistical downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions. Key Points: Explainable artificial intelligence (XAI) facilitates the evaluation of deep downscaling models by unraveling their internal behaviorXAI techniques can detect structural problems not revealed by standard evaluation, which may be relevant for understanding model differencesXAI techniques can assess the relevance and locality of the predictors of deep learning models, helping to assess their physical consistency [ABSTRACT FROM AUTHOR]

Thunderstorms are usually accompanied by heavy precipitation and other disasters. Therefore, it is very important to study the thermodynamic and dynamic mechanisms that control the occurrence and development of storms. However, the relatively coarse resolution of the reanalysis may limit the evaluation and application of its derived convective parameters at regional and local scales, especially in high mountains. Given that the effect of grid averaging on ERA5 can be well-reflected in the monthly mean, the paper proposes a methodological framework to achieve the statistical downscaling of convective parameters (SDCP). After the convection-related variables are decomposed, we introduce a multi-scale geographically weighted regression (MGWR) model and take the Normalized Difference Vegetation Index (NDVI) and the Digital Elevation Model (DEM) as explanatory variables to improve the spatial resolution of monthly-scale convective parameters. The convective parameters chosen for the downscaling experiments are closely related to thunderstorm occurrence, involving instability, moisture, and wind shear. ERA5 and SDCP datasets' quality is evaluated based on measurements available from 11 radiosonde stations over 10 years. We also explore the climatology of thunderstorm environments and the corresponding trends. The results show that the accuracy of the SDCP data obtains additional added value in complex-terrain areas. Compared with ERA5, the downscaled results have a finer spatial structure. A strong but spatially and scale-varying relationship exists between the monthly mean convective parameter and the resampled NDVI and DEM. Considering the very high predictive ability of the MGWR model, the performance of the SDCP algorithm mainly depends on the accuracy of the ERA5 data. Our study demonstrates the possibility of high spatial resolution land surface features observed by remote sensing satellites in improving the spatial resolution of thermodynamic parameters. [ABSTRACT FROM AUTHOR]

The study proposes a statistical downscaling procedure for regional rainfall. In the multistage procedure, the spatial downscaling stage using Relevance Vector Machine (RVM) model captures the climate change signals in the monthly scale simulations from the climate models by considering a predictor set that is based on the regional climatic phenomena. The spatially downscaled monthly rainfall is then disaggregated into daily scale using a modified weather generator. In the temporal downscaling stage using a weather generator, the transition matrix is modified to account for the non-stationarities in the rainfall. The proposed methodology is validated in the Bharathapuzha River Basin, India by downscaling rainfall data from climate models, BNU-ESM, CESM1-BGC, CMCC-ESM2, FGOALS-G2, FIO-ESM-2.0, and MIROC4H. The process-based indices specified in the VALUE framework developed from the downscaled rainfall data closely matches the indices developed from the observed rainfall data for the historical period, with absolute PBIAS less than 14% across all the indices considered. The reduction in uncertainty achieved is studied using average band width of the simulations, which is seen to reduce from 186 mm to 52 mm after downscaling. The capability of the downscaling procedure for capturing the non-stationarity in the climate is tested by comparing the performance of the procedure over warm and cold phases of ENSO, and it is found to be satisfactory. Highlights: The study proposes a multi stage, stochastic approach for statistical downscaling of rainfall. It combines the strength of RVM models and weather generators. The statistical downscaling relationship is based on the regional climatic phenomena. Downscaled rainfall series captures the occurrence and distribution characteristics of regional observed rainfall and its non-stationarity. [ABSTRACT FROM AUTHOR]

In this study, hydrological modeling at the watershed level is used to assess the impacts of climate and land use changes on the catchment area of the Khanpur Dam, which is an important water source for Rawalpindi and Islamabad. The hydrological impact of past and anticipated precipitation in the Khanpur Dam watershed was forecast by using a HEC-HMS model. After calibration, the framework was employed to analyze the effects of changes in land cover and climate on the hydrological regime. The model used information from three climatic gauge stations (Murree, Islamabad Zero Point, and Khanpur Dam) to split the Khanpur Dam catchment area into five sub-basins that encompass the entire watershed region, each with distinctive characteristics. The model was evaluated and checked for 2016–2018 and 2019–2020, and it produced an excellent match with the actual and anticipated flows. After statistical downscaling with the CMhyd model, the most effective performing GCM (MPI-ESM1-2-HR) among the four GCMs was chosen and used to forecast projections of temperature and precipitation within two shared socioeconomic pathways (SSP2 and SSP5). The predictions and anticipated changes in land cover were incorporated into the calibrated HEC-HMS model to evaluate the potential impact of climate change and land cover change at the Khanpur Dam. The starting point era (1990–2015) and the projected period (2016–2100), which encompassed the basis in the present century, were analyzed annually. The results indicated a spike in precipitation for the two SSPs, which was predicted to boost inflows all year. Until the end of the twenty-first century, SSP2 predicted a 21 percent rise in precipitation in the Khanpur Dam catchment area, while SSP5 predicted a 28% rise in precipitation. Increased flows were found to be projected in the future. It was found that the calibrated model could also be used effectively for upcoming studies on hydrological effects on inflows of the Khanpur Dam basin. [ABSTRACT FROM AUTHOR]

Climate model (CM) statistical downscaling requires quality and quantity modifications of the CM's outputs to increase further modeling accuracy. In this respect, multi-resolution wavelet transform (WT) was employed to determine the hidden resolutions of climate signals and eliminate bias in a CM. The results revealed that the newly developed discrete wavelet transform (DWT)-based bias correction method can outperform the quantile mapping (QM) method. In this study, wavelet coherence analysis was utilized to assess the high common powers and the multi-scale correlation between the predictors and predictand as a function of time and frequency. Thereafter, to rate the most contributing predictors based on potential periodicity, the average variance was calculated, which is named the Scaled Average (SA) measure. Consequently, WT along with Artificial Neural Network (ANN) were applied for bias correction and identifying the dominant predictors for statistical downscaling. The CAN-ESM5 data of Canadian climate models and INM-CM5 data of Russian climate models over two climatic areas of Iran with semi-arid (Tabriz) and humid (Rasht) weather were applied. The projection of future precipitation revealed that Tabriz will experience a 3.4–6.1% decrease in precipitation, while Rasht's precipitation will decrease by 1.5–2.5%. These findings underscore the importance of refining CM data and employing advanced techniques to assess the potential impacts of climate change on regional precipitation patterns. [ABSTRACT FROM AUTHOR]

Human activity has increased the amount of carbon dioxide and other greenhouse gases emitted into the atmosphere, causing climate change. As a result, rising temperatures have wide-ranging consequences on water management. This study proposes downscaling daily temperature based on a modified Classification and Regression Technique with an ensemble machine learning (EML) approach at the Woodstock station in the Upper Thames River basin. The GCM Canadian Earth System Model (CanESM5) from Coupled Model Intercomparison Project-6 is used. The CanESM5 model simulated variables are used as predictors and observed baseline daily temperature as predictands. The Regression-based single machine learning (Support Vector, Tree-based and Gaussian Process Regression) and EML based statistical downscaling are applied and compared. The variable temperature states are determined using Gaussian Mixture Model clustering, and the Light Gradient Boosting Model (LightGBM) is used to classify future temperature states. Results showed that applying the EML boosted the performance by 2–25% compared to single models. The temperature states for the two projected climate scenarios (SSP126 and SSP585) were simulated by selected best-performing single and EML model combinations for the near (2026–2050) and far future (2076–2100). The findings demonstrate that the future projected temperatures may rise 1–3 °C for both scenarios and are less volatile than the observed baseline temperature. Overall, the study indicates that the ensemble approach-based downscaling combining several single models have considerably improved the performance and was more reliable. [ABSTRACT FROM AUTHOR]

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.