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The seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles. [ABSTRACT FROM AUTHOR]

A homogenized daily mean temperature series from 1915 to 2021 at Beijing Observatory (BO) has been developed. A thorough investigation of observation records and historical metadata was carried out to identify specific non‐climatic biases. The inhomogeneities were detected and adjusted with the optimally selected reference stations using a statistical reduction methodology. The results indicated three types of non‐climatic biases in temperature records: the first was the relocation of BO from suburban to urban, which caused positive shifts in temperature records, especially for wintertime; the second arose from different methods for calculating daily mean temperature, which caused different sign biases varying with months; and the last was caused by the transition from manual to automatic measurements with site‐specific biases. The corresponding adjustments due to three type biases ranged from −1.13 to 0.63, −0.29 to 0.23 and −0.13 to 0.00°C, respectively. The new homogenized annual mean temperature series showed a warming trend of 0.199°C/decade during 1915–2021. The trend was more pronounced in winter than in the other three seasons. The warming trend in the new series is greater than those in the previous homogenized series (0.136–0.177°C/decade), primarily due to the more effective adjustment associated with the site relocation, particularly for more urban locations. [ABSTRACT FROM AUTHOR]

Long‐term changes in daily high‐temperature extremes in the summers since the late 19th century in Shanghai and their relationships with decadal or multi‐decadal climatic oscillations have been investigated via non‐stationary statistical modelling based on parametric and nonparametric frameworks. Compared to stationary modelling with the assumption of an unchanging climate, time‐varying non‐stationary models based on the generalized extreme value (GEV) result in much more reasonable estimations of the temperature extremes for the present climate in terms of their return levels and return periods. However, nonparametric modelling with cubic spline smoothing reproduced even better multi‐decadal variability for different periods. The nonparametric method also reduces the biases of estimation, especially in the heavy tail of temperature extremes in parametric GEV models. Additionally, non‐stationary models with large‐scale climate indices indicate clear influences of the large‐scale climate indices, especially the last winter's strong signals from the tropical Pacific Ocean and Indian Ocean. The significant influences of multi‐decadal variabilities from the northern Atlantic in the parametric GEV model are probably also explained by spline terms in the nonparametric method. Further application to other areas in China indicates that the developed nonparametric model can fit their extremes correctly but notable biases also remain in the estimation of distributions and heavy tails. [ABSTRACT FROM AUTHOR]

Key Points: Unprecedented compound heat waves will occur on a regular basis in three urban agglomerations in China since mid‐21st century under RCP8.5The likelihood of unprecedented compound heat waves in a 1.5°C warmer world will halve compared to 2°C warming scenarioPopulation exposures to future unprecedented compound heat waves will increase by nearly 70, 90, and 60 million by the end of this century Accounting for only a limited fraction of Earth's land surface, urban areas accommodate more than half the global population. The projected increasing severe heat waves with global warming exert a profound threat to the dense urban population and infrastructure. Despite abundant past studies on heat waves, there was a lack of attention to the daytime‐nighttime compound heat waves. Here, we categorize summertime heat waves into three distinct types, that is, independent daytime or nighttime heat waves and compound heat waves. Using a universal heat wave metric, we identify the strongest compound heat waves on record (1961–2015) in three vast urban agglomerations in China. We demonstrate substantial increase of the land areas affected by severe compound heat waves over the past three decades. We further quantify the changes in areal and population exposures to future unprecedented compound heat waves. Our results show that under a high‐end emission scenario, 50% (100%) of the land area in the Beijing‐Tianjin‐Hebei region, the Yangtze River Delta, and the Pearl River Delta will be exposed to historically unprecedented compound heat waves on a regular basis by 2050 (2090), 2050 (2070), and 2030 (2050), respectively. Such enhancing heat hazard will induce increasing population exposure of nearly 70, 90, and 60 million to unprecedented compound heat waves by the end of this century (relative to the 2010s). Our findings call for effective mitigation and adaptation strategies to alleviate the risks of unprecedented compound heat waves in rapidly developing populous urban areas. Plain Language Summary: Extreme heat waves impose devastating impacts on human health, economy, and the environment. The risk of extreme heat stress tends to be higher in urban areas than in surroundings, due to greater population exposure and added heat stress from urban heat island. Compared to daytime‐ or nighttime‐only heat waves, the risk of compound heat waves that combine scorching days and sweltering nights sequentially tends to be higher. Focusing on top three populous urban agglomerations in China, this study dissects summertime heat waves into three nonoverlapping types and identifies the strongest heat waves on record based on a universal metric of heat wave magnitude. Projections show that unprecedented compound heat waves will become the norm since around 2045 (2060), 2045 (2065), and 2030 (2040) in the Beijing‐Tianjin‐Hebei region, the Yangtze River Delta, and the Pearl River Delta, respectively, under RCP8.5 (RCP4.5) emission scenario. Enhancing heat hazards will translate into increasing population exposure of about 70, 90, and 60 million to unprecedented compound heat waves by the end of this century, which are concentrated on the highly urbanized areas, such as Beijing, Shanghai, Guangzhou, and Shenzhen. This study highlights the urgent adaptation and mitigation efforts for cities against compound heat waves in particular. [ABSTRACT FROM AUTHOR]

Prominent slowdown in near‐surface wind speed (NSWS) has been observed in the Beijing‐Tianjin‐Hebei (BTH) region of China, which has important implications for regional haze mitigation and wind energy production. Other than large‐scale climatic variation, this decline is suspected of being linked to the impact of urbanization. Based on nested high‐resolution simulations using the Weather Research and Forecasting model, we assessed quantitatively the respective roles of urban development and anthropogenic heat (AH) in regional NSWS change over the BTH region from a climatological perspective. The contribution of urbanization to station average annual NSWS over 1980–2018 was estimated to be approximately −0.37 m s−1, with the largest decline exceeding −1.0 m s−1 in some highly urbanized areas. The effect of urban development on NSWS was found partly offset by the AH effect. Urbanization‐induced NSWS decrease was most remarkable in spring and early in the night. Urbanization impacts on NSWS were more remarkable when regional prevailing winds were northeasterly and northwesterly. Further analysis showed that the simulated urbanization‐induced decreases in NSWS were highly correlated with the background regional NSWS levels. Our simulation results indicated that urbanization was an important but not a dominant cause of the observed regional wind stilling, even in such a rapidly urbanizing region, although it had remarkable influence on local NSWS in highly urbanized areas. Key Points: The contribution of urbanization to station average annual near‐surface wind speed over 1980–2018 was estimated to be about −0.37 m s−1Urbanization‐induced decrease in near‐surface wind speed was most remarkable in spring and early in the nightThe effect of urban development on annual near‐surface wind speed was found partly offset by the effect of anthropogenic heat [ABSTRACT FROM AUTHOR]

Seasonal prediction of extreme precipitation has long been a challenge especially for the East Asian Summer Monsoon region, where extreme rains are often disastrous for the human society and economy. This paper introduces a decision‐tree (DT) method for predicting extreme precipitation in the rainy season over South China in April–June (SC‐AMJ) and the North China Plain in July–August (NCP‐JA). A number of preceding climate indices are adopted as predictors. In both cases, the DT models involving ENSO and NAO indices exhibit the best performance with significant skills among those with other combinations of predictors and are superior to their linear counterpart, the binary logistic regression model. The physical mechanisms for the DT results are demonstrated by composite analyses of the same DT path samples. For SC‐AMJ, an extreme season can be determined mainly via two paths: the first follows a persistent negative NAO phase in February–March; the second goes with decaying El Niño. For NCP‐JA, an extreme season can also be traced via two paths: the first is featured by "non El Niño" and an extremely negative NAO phase in the preceding winter; the second follows a shift from El Niño in the preceding winter to La Niña in the early summer. Most of the mechanisms underlying the decision rules have been documented in previous studies, while some need further studies. The present results suggest that the decision‐tree approach takes advantage of discovering and incorporating various nonlinear relationships in the climate system, hence is of great potential for improving the prediction of seasonal extreme precipitation for given regions with increasing sample observations. [ABSTRACT FROM AUTHOR]

The changes in the frequency, duration, and intensity of summer heatwaves over central Asia during the period 1917–2016 were studied. On average, the frequency, duration, and intensity of heatwaves showed significant positive trends during the period 1917–2016, with enhanced rates during the last 50 years. During 1967–2016, the heatwave indices increased significantly in most of central Asia, especially in the western part. The number of heatwaves has increased by 1.3 times since the 1960s. Remarkable changes in the frequency and duration of heatwaves occurred during the 1990s in association with the inter‐decadal shift in the Silk Road pattern of atmospheric circulation around 1997. The results based on the ERA‐Interim reanalysis data set were well‐matched with the station observations during the period 1979–2016, whereas those based on the NCEP‐NCAR data set were less well matched. Heatwaves in central Asia were closely related to a zonal wave circulation pattern at 500 hPa with a centre of positive geopotential height anomalies over central Asia. This anomalous circulation pattern was rapidly enhanced during the 1990s, suggesting that large‐scale patterns of atmospheric circulation had a role in modulating the occurrence of heatwaves in central Asia. [ABSTRACT FROM AUTHOR]

Abstract: The observation minus reanalysis (OMR) method is widely used to investigate the impact of urbanization and land use changes on regional climate. Based on homogenized temperature observations and the National Centers for Environmental Prediction/Department of Energy Atmospheric Model Intercomparison Project II reanalysis surface temperatures, we estimate the trend in diurnal temperature range (DTR) using the OMR method to be −0.324 ± 0.063 °C per decade for the period of 1980–2009 in eastern China. However, we find that reanalysis data produce a positive trend in downward surface solar radiation, while the observations do not, because the reanalysis data significantly overestimate the negative trend in total cloud coverage in the region. Moreover, the reanalysis considerably overestimates the negative trend in the local precipitation. These systematic biases cause an overestimation of the warming rate in daily maximum temperature in the reanalysis and therefore lead to the exaggerated effect of urbanization on the DTR via the OMR analysis. After adjusting the systematic bias in the trend of daily maximum temperature in the reanalysis data, the regional mean OMR trend in the DTR is estimated to be −0.065 °C per decade, which becomes much smaller than previous OMR calculations, and is more consistent with the previous estimates based on the comparison of urban and rural stations. These results not only indicate the methodological problem of the OMR method in many studies of urbanization effects but also hint at important aspects for improving the current reanalysis system. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we conducted nested high‐resolution simulations using the Weather Research and Forecasting model coupled with a single‐layer urban canopy model to investigate the impact of extensive urbanization on regional precipitation over the Beijing‐Tianjin‐Hebei region in China. The results showed that extensive urbanization decreased precipitation considerably over and downwind of Beijing city. The prevalence of impermeable urban land inhibits local evaporation that feeds moisture into the overlying atmosphere, decreasing relative humidity and atmospheric instability. The dynamic precipitation recycling model was employed to estimate the precipitation that originates from local surface evaporation and large‐scale advection of moisture. Results showed that about 11% of the urbanization‐induced decrease in total precipitation over the Greater Beijing Region and its surroundings was contributed by the decrease in local recycled precipitation, while the other part (89%) was due to decreasing large‐scale advected precipitation. Results suggest that the low evaporation from urban land surfaces not only reduces the supply of water vapor for local recycled precipitation directly but also decreases the convective available potential energy and hence the conversion efficiency of atmospheric moisture into rainfall. The urbanization‐induced variations in local recycled precipitation were found to be correlated with the net atmospheric moisture flux on a monthly time scale. [ABSTRACT FROM AUTHOR]

ABSTRACT: The geographical extent, magnitude, and uncertainty of global climate change have been widely discussed and have critical policy implications at both global and local scales. In this study, a new analysis of annual mean global land surface air temperature since 1880 was generated, which has greater coverage and lower uncertainty than previous distributions. The Biased Sentinel Hospitals Areal Disease Estimation (BSHADE) method, used in this study, makes a best linear unbiased estimation (BLUE) when a sample is small and biased to a spatially heterogeneous population. For the period of 1901–2010, the warming trend was found to be 0.109 °C decade−1 with 95% confidence intervals between 0.081 °C and 0.137 °C. Additionally, warming exhibited different spatial patterns in different periods. In the early 20th century (1923–1950), warming occurred mainly in the mid‐high latitudes of the Northern Hemisphere, whereas in the most recent decades (1977–2014), warming was more spatially extensive across the global land surface. Compared with other common methods, the difference in results appears in the areas with few stations and in the early years, when stations had sparse coverage and were unevenly distributed. Validation, which was performed using real data that simulated the historic situation, showed a smaller error in the BSHADE estimate than in other methods. This study produced a new database with greater coverage and less uncertainty that will improve the understanding of climate dynamics on the Earth since 1880, especially in isolated areas and early periods, and will benefit the assessment of climate‐change‐related issues, such as the effects of human activities. [ABSTRACT FROM AUTHOR]

ABSTRACT An 'alternative multi-model ensemble mean' ( AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle ( MAC), multi-decadal variability ( MDV), a secular trend ( ST), and short-term variability ( SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H- CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. [ABSTRACT FROM AUTHOR]

ABSTRACT A new homogenized temperature data set called the China Homogenized Historical Temperature Dataset ( CHHTD-V1.0) has been developed, and it includes daily and monthly mean temperature series from 2419 national stations distributed throughout mainland China for the period from 1951 to the present. The inhomogeneities in individual station series were detected using a penalized maximum t-test ( PMT) that accounted for the first-order autocorrelation. Detailed metadata information was applied to validate the changepoints caused by changes in local observation systems. Comparative analyses suggested that the quantile-matching ( QM) adjustments that accounted for high-order discontinuities led to more reasonable results than the MEAN adjustments for the daily temperature series. Therefore, the QM method was applied to adjust the discontinuities caused by non-climate changes such as changing of observing site, instrumentation and observation environments. The physical consistency among the daily maximum, mean and minimum temperatures ( Tmax, Tm and Tmin) was also checked for each station. Based on the new homogenized data set, linear trends in the annual and seasonal temperature series from 1960 to 2014 were calculated. In comparison with the original data set, the homogenized data set improves the geographical consistency of the long-term climate trends over the region. The updated nationwide mean warming rate reached 0.22 °C per 10 years for the Tmax, 0.27 °C per 10 years for the Tm and 0.38 °C per 10 years for the Tmin from 1960 to 2014, which are considerably larger than the previous estimates that were based on the more frequently used networks of a few hundred stations in China. [ABSTRACT FROM AUTHOR]

ABSTRACT Eastern China suffered a record-breaking heat wave throughout almost all of summer, during 2013. Recent studies found a discernible impact of anthropogenic forcing on this extreme heat wave event. In this study, we investigate the role of multidecadal variability ( MDV) in regulating the likelihood of the 2013 heat wave event. Using the ensemble empirical mode decomposition ( EEMD) method, we decomposed the heat wave index series into three components: (1) a non-linear secular trend ( ST), representing the long-term anthropogenic warming; (2) MDV; and (3) the residual high-frequency variability. For the period 1873-2013, the linear trend of heat wave strength ( HW-S, defined as the July-August mean temperatures) was 0.06 °C per decade, mainly because of the contribution of ST. MDV plays an important role in regulating changes over decades. ST and MDV together form a changing climate background ( CB) for extreme events. The 2013 heat wave event would be almost impossible under the pre-industrial CB but becomes likely under the long-term warming background, with a return period of longer than 103 years. When the positive phase of MDV is added to the secular warming background (the current CB), the return period of a super-heat wave such as the 2013 event becomes about 42 (12-103) years. The Atlantic Multidecadal Oscillation ( AMO) shows significant correlation with the MDV component of the HW-S in Shanghai. The correlation pattern between the AMO and MDV of July-August temperatures over the Northern Hemisphere is analysed to explain the AMO-Shanghai relationship. It is suggested that the likelihood of such an extreme event will increase with further long-term climate warming, modified by low-frequency oceanic variations such as the AMO. [ABSTRACT FROM AUTHOR]

ABSTRACT Two circulation index-based methods were applied to downscale summer daily rainfall at four meteorological stations in Chongqing, China. One is a regression method ( CIM) and the other a hybrid circulation classification plus regression method ( CPM). Both methods used the same three circulation indices and surface specific humidity as predictors. In the first method ( CIM), the indices and humidity were used directly as predictors and only one model was developed at each station. In the second method ( CPM), the indices were further used to define circulation patterns using the objective Lamb-Jenkinson classification scheme, and one model was built using humidity as the sole predictor for each pattern. Logistic regression was used to define rainfall probability, and a gamma distribution was fit using observations to randomly generate daily rainfall amounts. The two downscaling methods were validated and compared. The results suggest that (1) both methods yield consistently reasonable results with respect to occurrence and amount of daily rainfall, although they perform poorly in reproducing interannual and interdecadal variability, (2) specific humidity should be used as a predictor. Both methods were forced by two future scenarios of a global climate model ( GCM) to demonstrate the added value with two methods. The downscaled scenarios show a shift towards larger rainfall values, accompanied by more frequent dry days. This shift is mainly attributed to a change in specific humidity. Despite the similar performances of the two methods, CPM gives a higher frequency of dry days than CIM, whereas CIM produces stronger intensity of rainfall than CPM. This provides additional information about the uncertainty in the projections. [ABSTRACT FROM AUTHOR]

A wavelet-analysis-based homogenization method (WHM) is developed for detecting and adjusting biases of variability in a daily climate observation series, which are influential in the estimation of climate extremes and relevant trends in the time series. WHM is applied to the Central England daily mean temperature series (CET, 1772-2010) and the daily mean temperature series at Henan station in mountainous western China (HNT, 1960-2008) in order to demonstrate the usefulness of the new technique. The changes of methods for calculating daily mean temperature for CET in 1878 caused significantly reduced daily variability (DV) in the subsequent sub-series of CET. The relocation of the Chinese station in 1981 to a higher mountainous site resulted in not only enhanced DV but also in weather (weekly) time-scale variability ( WV) in the subsequent sub-series of HNT. The adjustments based on WHM in the present cases improve the estimates of the long-term trends of climate extremes such as hot days, cold days, heat waves, and cold surges. A few widely applied methods of homogenization such as multiple analysis of series for homogenization ( MASH), higher-order moment (HOM), RHtestsV3 with quantile-matching (QM) adjustments and two-phase regression (TPR) are also applied to HNT for comparison. TPR is not aimed at improving the homogeneity of variability in the time series; MASH does not improve daily variability either; HOM improves it in a small way; and with RHtestsV3 with QM adjustments it is considerably improved, but biases remain. In contrast, WHM improves the homogeneity of the short-term variability in the time series, resulting in reasonable assessments of climate extremes and trends in the daily observations. [ABSTRACT FROM AUTHOR]