In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information.

Climate change has dramatic impacts on the growth and the geographical distribution of tea (Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage from climate hazards. The objective in this study is to model the current and future distribution of tea species based on the four SSPs scenarios using the MaxEnt model in China. For the modeling procedure, tea growth records in 410 sites and 9 climate variables were used in this paper. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the model. The AUC value was over 0.9 in this study, showing the excellent simulation result of the model. In relation to the current distribution, areas of 82.01 × 10⁴ km² (8.51% of total land area in China), 115.97 × 10⁴ km² (12.03% of total land area in China), and 67.14 × 10⁴ km² (6.97% of total land area in China) were recognized as Marginal, Medium, and Optimal climate suitable habitats for tea over China. Compared to the current distribution, most of the Optimal suitability areas in southeast China would be lost in four scenarios. The area of Marginal and Medium suitable habitats would expand in SSP370 and SSP585, especially in 2041–2061 and 2081–2100. The suitable area of tea would expand northwards and westwards, suggesting that additional new suitable habitats could be created for tea production with the future climate change, especially in Shandong, Henan, Guizhou, and Yunnan Provinces. This research would provide vital scientific understanding for policy making on tea production, tea garden site chosen and adopyion of adaptation methods in the future.

Tea (Camellia sinensis L.), as one of the most important cash crops in China, plays an important role in increasing farmers’ incomes and guaranteeing a high quality of life. Tea production has been greatly influenced by both climate change and economic development in China. However, without a scientific understanding of the interaction mechanism of climate change and the impetus from rapid economic development on tea production practices in China, it is difficult to take adaptive actions to meet the climate change challenges for the tea industry. In this paper, we firstly assessed the potential impacts of climate change on tea climate suitability by empirical formula calculation using meteorological data; then, the effects from the additional climatic stress due to warming on tea production were detected with the annual statistical tea yield record on a municipal level. The contribution of socioeconomic development to the tea industry was evaluated with the comparison of the movement of China’s national economy’s and tea industry’s gravity center during the period of 1987–2017. Finally, a conceptual adaptation framework was built to demonstrate the interaction mechanisms between climate change, tea production, and the economic development. The results showed that there was a negative impact of climate change on tea production in mainland China, with the percentage of high tea climate suitability (>0.9) areas dropping by 45% to 32%, while opportunities of enlarging the tea cultivating area emerged in the north tea production region where the tea climate suitability increased. We found that the tea planting area expanded northwards from 33° N in 1987 to 35° N in 2017 to take advantage of the favorable climatic resources due to warming, and tea planting decreased at an altitude of 100–400 m while increasing to higher altitude of 400–2000 m to avoid hot temperature damage and seek the optimum environment in high mountainous areas for tea production. In addition, the tea production moved westward along the longitude, decreasing obviously at 117–121° E while increasing significantly at 98–104° E and 107–110° E. Meanwhile, the tea production gravity center showed a westward movement consistent with the national economic gravity center moving trend, which means that tea industry development was driven by multiple socioeconomic factors and climatic forcings. A conceptual framework was built in this paper, aiming to show a robust adaptation mechanism for the tea system to maximize the benefits and minimize the damages from the altered climatic resources under rapid economic development in mainland China. The results in this study would help deepen the understanding of the adaptation process and practices for tea production in mainland China.

Global warming poses a severe threat to food security in developing countries. In Ethiopia, the primary driver of low wheat productivity is attributed to climate change. Due to the sparsity of observation data, climate-related impact analysis is poorly understood, and the adaptation strategies studied so far have also been insufficient. This study adopted the most popular DSSAT CERES-Wheat model and the ensemble mean of four GCMs to examine the quantitative effects of adjusted sowing dates and varieties on wheat yield. The two new cultivars (Dandaa and Kakaba), with reference to an old cultivar (Digelu), were considered for the mid-century (2036–2065) and late-century (2066–2095) under RCP 4.5 and RCP 8.5 climate scenarios. The results showed that the Dandaa cultivar demonstrates better adaptation potential at late sowing with a yield increase of about 140 kg/ha to 148 kg/ha for the mid- and late-century under RCP4.5. However, under RCP 8.5, Kakaba demonstrates higher adaptation potential with a yield gain for early sowing of up to 142 kg/ha and 170 kg/ha during the mid- and late-century, respectively. Late sowing of the Dandaa cultivar is recommended if GHG emissions are cut off at least to the average scenario, while the Kakaba cultivar is the best option when the emissions are high. The adaptation measures assessed in this study could help to enhance wheat production and adaptability of wheat to the future climate.

Temperature extremes can cause disastrous impacts on ecological and social economic systems. China is very sensitive to climate change, as its warming rate exceeds that of the global mean level. This paper focused on the spatial and temporal changes of the temperature extremes characterized by the 95th percentile of maximum temperature (TX95), the 5th percentile of the minimum temperature (TN5), high-temperature days (HTD) and low-temperature days (LTD). The daily maximum and minimum temperatures generated by PRECIS under different Representative Concentration Pathways (RCPs) are used in the research. The results show that: (1) Model simulation data can reproduce the spatial distribution features of the maximum temperature (Tmax) and minimum temperature (Tmin) as well as that of the extreme temperature indices; (2) By the end of the 21st century (2070–2099), both the Tmax and Tmin are warmer than the baseline level (1961–1990) in China and the eight sub-regions. However, there are regional differences in the asymmetrical warming features, as the Tmin warms more than the Tmax in the northern part of China and the Tibetan Plateau, while the Tmax warms more than the Tmin in the southern part of China; (3) The frequency of the warm extremes would become more usual, as the HTD characterized by the present-day threshold would increase by 106%, 196% and 346%, under RCP2.6, RCP4.5 and RCP8.5, respectively, while the cold extremes characterized by the LTD would become less frequent by the end of the 21st century, decreasing by 75%, 90% and 98% under RCP2.6, RCP4.5 and RCP8.5, respectively. The southern and eastern parts of the Tibetan Plateau respond sensitively to changes in both the hot and cold extremes, suggesting its higher likelihood to suffer from climate warming; (4) The intensity of the warm (cold) extremes would increase (decrease) significantly, characterized by the changes in the TX95 (TN5) by the end of the 21st century, and the magnitude of the increase in the TN5 is larger than that of the TX95 in the national mean value. The changes in both the TX95 and TN5 are larger under the higher versus weaker emission scenario, indicating that it is essential to reduce the GHG emissions and take adaptation measures in the future.

As global temperatures continue to rise unabated, episodes of heat-related catastrophes across the world have intensified. In Kenya, heatwave phenomena and their associated impacts are ignored and neglected due to several reasons, including unreliable and inconsistent weather datasets and heatwave detection metrics. Based on CHIRTS satellite infrared estimates and station blended temperature, this study investigated the spatiotemporal distribution of the heatwave events over Kenya during 1987–2016 using the Heatwave Magnitude Index daily (HWMId). The results showed that contrary to the absence of heatwave records in official national and international disaster database about Kenya, the country experienced heatwaves ranging from less severe (normal) to deadly (super-extreme) between 1987 and 2016. The most affected areas were located in the eastern parts of the country, especially in Garissa and Tana River, and in the west-northern side around the upper side of Turkana county. It was also found that the recent years’ heatwaves were more severe in magnitude, duration, and spatial extent. The highest magnitude of the heatwaves was recorded in 2015 (HWMId = 22.64) while the average over the reference period is around 6. CHIRTS and HWMId were able to reveal and capture most critical heatwave events over the study period. Therefore, they could be used respectively as data source and detection metrics, for heatwaves disaster emergency warning over short period as well as for long-term projection to provide insight for adaptation strategies.