Surface air temperature outputs from 16 global climate models participating in the sixth phase of the coupled model intercomparison project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901–2014. Projections of multi-model mean under four different shared socioeconomic pathways were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial variations in observed temperature with a pattern correlation typically greater than 0.98, but with large variability across models and regions. In addition, the CMIP6 mean can capture the trends of global surface temperatures shown by the observational data during 1901–1940 (warming), 1941–1970 (cooling) and 1971–2014 (rapid warming). By the end of the 21st century, the global temperature under different scenarios is projected to increase by 1.18 °C/100 yr (SSP1-2.6), 3.22 °C/100 yr (SSP2-4.5), 5.50 °C/100 yr (SSP3-7.0) and 7.20 °C/100 yr (SSP5-8.5), with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere. Results of probability density distributions further indicate that large increases in the frequency and magnitude of warm extremes over the global land may occur in the future.

Simulations from the models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), which represent the most recent generation of climate models, are now available. Understanding the performance of these models in simulating historical climate extremes can provide a basis for producing reliable future climate projections. Here, we assess the simulation of 16 indices of temperature extremes defined by the Expert Team on Climate Change Detection and Indices using results from 24 CMIP6 models as compared with results from CMIP5. Comparisons with observations and reanalyses indicate that the CMIP6 models could capture the spatial patterns and temporal variations of the observed temperature extremes well for some indices, although less well for others. Based on spatial and temporal skill scores, CMIP6 ensemble means were more skillful in simulating absolute and threshold indices of extreme temperature than CMIP5 ensemble means were, but the performances of both the CMIP5 and CMIP6 ensemble means in simulating the spatial patterns for duration and percentile indices were relatively unsatisfactory (spatial skill scores S < 0.3). Furthermore, our results suggest that there have been improvements in spatial pattern skill scores in some individual CMIP6 models relative to CMIP5 model scores for summer days, tropical nights, cold spell duration, and diurnal temperature range.

Future climate change will not only affect mean climate but will also likely alter the frequency and magnitude of extreme climate events (Fischer & Knutti, 2015;Miao et al., 2014;. Here, we broadly define an "extreme" event as a rare and infrequent occurrence at a specific time and site. It is a low-probability event corresponding to a certain climate variable from either of the tails of that variable's probability density function (Seneviratne et al., 2012). Specifically, we usually define extreme events as those that occur in the highest or lowest 5% (

Identifying climate change hotspot regions is critical for planning effective mitigation and adaptation activities. We use standard Euclidean distance (SED) to calculate integrated changes in precipitation and temperature means, interannual variability, and extremes between different future warming levels and a baseline period (1995–2014) using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble. We find consistent hotspots in the Amazon, central and western Africa, Indonesia and the Tibetan Plateau at warming levels of 1.5°C, 2°C, and 3°C for all scenarios explored; the Arctic, Central America and southern Africa emerge as hotspots at 4°C warming and at the end of the 21st century under two Shared Socioeconomic Pathways scenarios, SSP3‐7.0 and SSP5‐8.5. CMIP6 models show higher SED values than CMIP5, suggesting stronger aggregated effects of climate change under the new scenarios. Hotspot time of emergence (TOE) is further investigated; TOE is defined as the year when the climate change signal first exceeds the noise of natural variability in 21st century projections. The results indicate that TOEs for warming would occur over all primary hotspots, with the earliest occurring in the Arctic and Indonesia. For precipitation, TOEs occur before 2100 in the Arctic, the Tibetan Plateau and Central America. Results using a geographical detector model show that patterns of SED are shaped by extreme hot and dry occurrences at low‐to‐medium warming, while precipitation and temperature means and extreme precipitation occurrences are the dominant influences under the high emission scenario and at high warming levels.

Quantifying a set of suitable physics parameterization schemes for the Weather Research and Forecasting (WRF) model is essential for obtaining highly accurate typhoon forecasts. In this study, a systematic Tukey-based combinatorial optimization method was proposed to determine the optimal physics schemes of the WRF model for 15 typhoon simulations over the Northwest Pacific Ocean, covering all available schemes of microphysics (MP), cumulus (CU), and planetary boundary layer (PBL) physical processes. Results showed that 284 scheme combination searches were sufficient to find the optimal scheme combinations for simulations of track (km), central sea level pressure (CSLP, hPa), and 10 m maximum surface wind (10-m wind, m s−1), compared with the 700 sets of full combinations (i.e., 10 MP × 7 CU × 10 PBL). The decrease in the typhoon simulation error (i.e., root mean square error between simulation and observations) with this optimal scheme combination was 34%, 33.92%, and 25.67% for the track, CSLP, and 10-m wind, respectively. Overall, the results demonstrated that the optimal scheme combination yields reasonable results, and the Tukey-based optimization method is very effective and efficient in terms of computational resources.

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