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 models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.

The EFRE-Project BigData@Geo, founded by the European Union, aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as the public. As recent regional climate models are not dealing with the necessary spatiotemporal resolution the model REMO will be developed in the project‘s frame in cooperation with the Climate Service Center Germany (GERICS).For these very high resolutions, besides improvements like the non-hydrostatic atmosphere, higher resolved static land surface parameters, and land use land cover changes, etc., realistic modeling of the soil hydrology becomes absolutely necessary. Therefore, REMO is extended by a 5-layer soil scheme which is a first step to overcome restrictions of the recently used soil hydrology scheme due to the included vertical water flow. Furthermore, the current work also aims to implement lateral water flows between grid cells because this is the only way to model the soil hydrology appropriate to the project‘s question.The current model version of REMO includes a bucket scheme that treats the soil hydrology as a single layer. The soil depth is equal to the rooting depth and, thus, depends on the overlying vegetation class. Consequently, the whole soil moisture of the soil is available for transpiration. Evaporation only occurs if the soil moisture reaches at least 90 % of the field capacity.The 5-layer scheme has 5 layers with increasing thicknesses for deeper layers. The maximum depth of the soil is at approximately 10 m or the depth of the bedrock. Due to the existence of water below the rooting zone and the processes of capillary rise and percolation more water becomes available for transpiration compared to the bucket scheme. Furthermore, evaporation only occurs if the uppermost layer contains soil moisture which is a more realistic process representation as well.First results of the comparison of the two schemes and with observation data in the EURO-CORDEX region and a german subregion are presented. We also show some sensitivity studies of the current improvements to the parameterizations of the 5-layer scheme which are necessary for the goal of incorporation of the lateral flow.

This study assesses the ability of climate models to represent rainy season (RS) dependent climate indices relevant for agriculture and crop-specific agricultural indices in eleven African subregions. For this, we analyze model ensembles build from Regional Climate Models (RCMs) from CORDEX-CORE (RCM_hist) and their respective driving General Circulation Models (GCMs) from CMIP5 (GCM_hist). Those are compared with gridded reference data including reanalyses at high spatio-temporal resolution (≤ 0.25°, daily) over the climatological period 1981–2010. Furthermore, the ensemble of RCM-evaluation runs forced by ERA-Interim (RCM_eval) is considered. Beside precipitation indices like the precipitation sum or number of rainy days annually and during the RS, we examine three agricultural indices (crop water need (CWN), irrigation requirement, water availability), depending on the RS’ onset. The agricultural-relevant indices as simulated by climate models, including CORDEX-CORE, are assessed for the first time over several African subregions. All model ensembles simulate the general precipitation characteristics well. However, their performance strongly depends on the subregion. We show that the models can represent the RS in subregions with one RS adequately yet struggle in reproducing characteristics of two RSs. Precipitation indices based on the RS also show variable errors among the models and subregions. The representation of CWN is affected by the model family (GCM, RCM) and the forcing data (GCM, ERA-Interim). Nevertheless, the too coarse resolution of the GCMs hinders the representation of such specific indices as they are not able to consider land surface features and related processes of smaller scale. Additionally, the daily scale and the usage of complex variables (e.g., surface latent heat flux for CWN) and related preconditions (e.g., RS-onset and its spatial representation) add uncertainty to the index calculation. Mostly, the RCMs show a higher skill in representing the indices and add value to their forcing models.

The study aimed to assess the impact of climate change on the crop suitability index (CSI) of selected staple crops for current (1981–2010) and future (2021–2050 and 2051–2080) climates across Africa. Precipitation and mean temperature data from gridded observations, and 10 Global Climate Models (GCMs) were utilized to calculate the CSI for maize, soybean, wheat, plantain, cassava, rice, millet, sorghum, and yam. The Ecocrop model implemented in R, utilizing the FAO‐Ecocrop database alongside climatic variables for different climatic zones across the continent, was employed to compute the CSI. The results indicate that all crops, except rain‐fed rice, are suitable in parts of West and Central African regions, with wheat being inclusive in some parts of the Guinea Coast. The northern, eastern, and southern African regions are identified as the least suitable for any crop production based on the balance between the base climate parameters over the historical period. Analysis over this historical period reveals an increasing trend for major crops in most regions, except for wheat crop production, which demonstrates a decreasing trend in most areas. Projection analysis reveals that the Sahel region is expected to be the most affected by climate change, with a significant reduction in the suitability index for most crops. Conversely, the Southeastern Africa and the Guinea Coast regions are likely to be the least affected, as the suitability index increases for the considered crops. This analysis provides crucial information for effective agricultural planning and resource allocation, optimizing land use by identifying crops aligned with prevailing environmental conditions, including soil type, climate, and water availability. Such information enhances the understanding of crop suitability, contributing to improved agricultural productivity and sustainability.

Unraveling plausible future rainfall change (Δ Pr ) patterns is crucial for tailoring societies' responses to climate change‐induced hazards. This study compares rainfall projections from the regionally coupled ocean model (ROM) and its atmospheric component, the regional atmospheric model REMO, over Central Equatorial Africa (CEA). Both models are forced by the Earth system model MPI‐ESM‐LR following the Representative Concentration Pathway 8.5. Results reveal increased rainfall across most of CEA, with ROM projecting more widespread and intensified wetting than REMO, although REMO produces more precipitation under future conditions, underscoring the influence of historical biases on REMO's projection. Examining processes underpinning changes unveils strong controls of sea and land surface temperature changes in Δ Pr differences between the two models. Specifically, ROM mitigates warming more over the Atlantic than over CEA landmass compared to REMO, inducing enhancement of the Congo Basin cell and increased precipitable water content through specific humidity, affecting deep convection. Both models project enhanced Sahel and Kalahari thermal lows, with ROM better depicting the Kalahari low's warmer nature than the Sahel low. The resulting temperature gradients strengthen the northern and southern shallow meridional Hadley overturning circulation. ROM simulates the wetter conditions than REMO, attributed to its weaker northern Hadley Cell, which restricts the likelihood of northward moisture divergence toward the Sahel. Additionally, differences in mid‐tropospheric moisture convergence differentiate between ROM and REMO's wetness relative to the historical period and under future conditions. ROM projections are more plausible, in association with the reliability of its added value under the historical climate and mechanisms underlying Δ pr .