Climate change continues to increase the intensity, frequency and impacts of weather and climate extremes. This work uses bias-adjusted Coupled Model Intercomparison Project Phase six (CMIP6) model datasets to investigate the future changes in temperature extremes over Mediterranean (MED) and Sahara (SAH) regions. The mid- (2041–2070) and far-future (2071–2100) are studied under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5 scenarios. Quantile mapping function greatly improved the performance of CMIP6 by reducing the notable biases to match the distribution of observation data, the Climate Prediction Center (CPC). Results show persistent significant warming throughout the 21st century, increasing with the increase in radiative forcing. The MED will record a higher increase in temperature extremes as compared to SAH. The warming is supported by the projected reduction in cold days (TX10p) and cold nights (TN10p), with the reduction in the number of cold nights exceeding cold days. Notably, warm spell duration index (WSDI) and summer days (SU) have a positive trend in both timelines over the entire study area. There is a need to simulate how climate sensitive sectors, such as water and agriculture, are likely to be affected by projected changes under different scenarios for informed decision making in the choice and implementation of adaptation and mitigation effective measures.

This study examines the improvement in coupled intercomparison project phase six (CMIP6) models against the predecessor CMIP5 in simulating mean and extreme precipitation over the East Africa region. The study compares the climatology of the precipitation indices simulated by the CMIP models with the CHIRPS dataset using robust statistical techniques for 1981 – 2005. The results display the varying performance of the general circulation models (GCMs) in the simulation of annual and seasonal precipitation climatology over the study domain. CMIP6-MME shows improved performance in the local annual mean cycle simulation with a better representation of two peaks, especially the MAM rainfall relative to its predecessor. Moreover, simulation of extreme indices is well captured in CMIP6 models relative to its predecessor. The CMIP6-MME performed better than the CMIP5-MME with lesser biases in simulating SDII, CDD, and R20mm over East Africa. Remarkably, most CMIP6 models are unable to simulate extremely wet days (R95p). A few CMIP6 models (e.g., NorESM2-MM and CNRM-CM6-1) depicts robust performance in reproducing the observed indices across all analyses. Conversely, OND season shows the overestimation of some indices (i.e., R95p, PRCPTOT), except for SDII, CDD, and R20mm. Consistent with other studies, the mean ensemble performance for both CMIP5/6 shows better performance due to the cancellation of some systematic errors in the individual models. Generally, the CMIP6 depicts improved performance in the simulation of MAM season akin CMIP5 models. However, the new model generation is still marred with uncertainty, thereby depicting substandard performance over the East Africa domain. This calls for further investigation of attribution studies into the sources of persistent systematic biases and a prerequisite for identifying individual models with robust features that can accurately simulate observed patterns for future usage.

Based on precipitation extremes calculated from high-resolution daily observational data in East China during 1961–2014, the performance of 34 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) are assessed in terms of climatology and interannual variability. Four extreme precipitation indices, including the total precipitation (Prcptot), the total precipitation for events exceeding the 95th percentile (R95p), and the maximum of 1-day (Rx1day) and 5-day (Rx5day) precipitation, are analyzed. Results show that the CMIP6 models demonstrate good performances in reproducing the climatological spatial distribution and interannual variability of precipitation extremes, with the best from Prcptot. Based on an integrated assessment of the above two factors, the models that perform relatively well for all four extreme precipitation indices are GFDL-CM4, MIROC6, EC-Earth3-Veg, EC-Earth3, and EC-Earth3-CC. Furthermore, the optimal multi-model ensemble (A-MME) constructed from a selection of the most skillful models shows improved behavior compared to the all-model ensemble. The wet (dry) biases over the northern (southern) region of East China are all decreased. This may benefit from the improvement that A-MME can reproduce well the characteristics of moisture and vertical velocity.

Under global warming, summer precipitation over East China was projected to increase by current state-of-the-art climate models, but large inter-model spread exists. Here we try to reduce the projection uncertainty by imposing constraints. Our procedure consists of firstly decomposing the projected future precipitation into inter-model principal components. The two leading modes (region-wide uniform monopole and north-south dipole, accounting for 55% of variability), by emergent constraints, are then linked to the simulation of historical precipitation in the northwest Pacific and the tropical Pacific (constraining areas). This allows us to reduce the range of projected precipitation in East China by 24%, with a relatively uniform increase for the first mode and a larger increase in the north subregion for the second. It is also shown that the emergent relations of both modes are physically consistent, with an enhancement of future zonal land-sea thermal contrast and a La Niña-like pattern.

This study investigated future changes in extreme climate events over China under carbon neutrality using an improved reliability ensemble averaging (REA) method, based on 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under the SSP1-1.9 and SSP1-2.6 scenarios. Results indicated that carbon neutrality is reached in 2042/2063 under SSP1-1.9/SSP1-2.6. Relative to 1995–2014, the areal-mean increases in annual mean temperature (Tav), hottest day temperature (TXx), coldest night temperature (TNn), total precipitation (Prcptot), and heavy precipitation (R95p) are 1.27/1.61 °C, 1.31/1.69 °C, 1.45/1.84 °C, 9.5/13.3%, and 20.8/29.6% under SSP1-1.9/SSP1-2.6, respectively. Larger increases in Tav and TNn occur in northeastern (NE) and northern China, while those in TXx occur in northwestern (NW) China. Temperature projections are most (least) robust in southeastern China (NE). Prcptot increases largely in the Tibetan Plateau (TB) and NE, while R95p increases largely in TB and south of the Yangtze River. The smallest but most robust precipitation increase is in southwestern China, while the least robust is in NW. The areal-mean Tav, TXx, and TNn increase by 0.34 °C, 0.38 °C, and 0.39 °C under SSP1-2.6 (late carbon neutrality) compared with SSP1-1.9 (early carbon neutrality), while those for Prcptot and R95p increase by 3.8% and 8.8%, respectively. Achieving carbon neutrality earlier may contribute to mitigating future climate change and reducing the worsening of extreme temperature and precipitation extremes over parts of China.