Drought is projected to intensify under warming climate and will continuously threaten global food security. Assessing the risk of yield loss due to drought is key to developing effective agronomic options for farmers and policymakers. However, little has been known about determining the likelihood of reduced crop yield under different drought conditions and defining thresholds that trigger yield loss at the regional scale in Australia. Here, we estimated the dependence of yield variation on drought conditions and identified drought thresholds for 12 Australia's key wheat producing regions with historical yield data by developing bivariate models based on copula functions. These identified drought thresholds were used to investigate drought statistics under climate change with an ensemble of 36 climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that drought-induced yield loss was region-specific. The drought thresholds leading to the same magnitude of wheat yield reduction were smaller in regions of southern Queensland and larger in Western Australia mainly due to different climate and soil conditions. Drought will be more frequent and affect larger areas under future warming climates. Based on our results, we advocate for more effective crop management options, particularly in regions where wheat yield is vulnerable to drought in Australia. This will mitigate potential drought impacts on crop production and safeguard global food security.

In this study, we examined the potential of the Korea Rapid-Development Thunderstorm (K-RDT) product obtained from a geostationary meteorological satellite to improve the short-term prediction of heavy rainfall caused by a mesoscale convective system over South Korea. Specifically, we utilized a simple nudging technique to integrate K-RDT data into the Simplified Arakawa Schubert (SAS) deep convection scheme of the Global/Regional Integrated Model System (GRIMs) Regional Model program (RMP). Our analysis focuses on selected cases of heavy rainfall. The nudging experiments outperformed the control experiments in terms of precipitation forecasts. Notably, the experiment that used longer nudging times produced the best results. Our results also demonstrate that the K-RDT, with its resolution of 1 km, can detect small-scale convective cells that have clear impacts on large-scale atmospheric fields. This suggests that incorporating such small-scale information into numerical weather prediction (NWP) models can significantly improve forecasting skill, especially when the model cannot represent subgrid-scale convection.

The increased risk of flooding due to global warming and subsequent heavy rainfall events in the Nordic-Baltic region, call for recommendations directed at long-term planning. One example of such recommendations are climate change allowances. These are often based on expected changes in design precipitation as given by climate model simulations, and are used as a buffer on top of current design values to avoid a future increased damage potential as a consequence of climate change. We here compute expected changes in precipitation design values, so-called climate factors (CFs), for the Nordic-Baltic region, based on 3 km convection permitting simulations from the Nordic Convection Permitting Climate Projections project. These have the advantage of explicitly resolving convection, which has been shown to be the main contributor to increased rainfall. We assess the dependence of the CFs on rainfall duration, return period, and geographical location, focusing on the summer (convective) season, short durations and the high emission scenario RCP8.5. We also compare these CFs to those computed from a non-convection permitting regional climate model ensemble.We found higher CFs for the longer return period, with only few exceptions, and distinctly higher CFs going from daily to sub-daily durations. However, the different simulations give conflicting results for very short-duration rainfall (<3 h). The huge difference in the climate sensitivity of driving GCMs dominates the magnitude of estimated return levels. Our analysis is shaped by the high computational costs of running convection permitting models, resulting in a very limited ensemble (3 members) representing a single emission scenario (RCP8.5). Therefore, we believe that combining results from convection permitting simulations with a larger ensemble (21 members) of non-convection permitting simulations adds value to the assessment of robust climate change allowances for heavy precipitation in the Nordic-Baltic region.