Climate reanalyses combine historical observational data with advanced modeling techniques to create long-term, consistent climate datasets. Such global datasets are produced by several international centers and have a large and diverse range of applications. The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system and coordinates the generation and exchange of weather, climate, and water information across its members. The WMO has successfully coordinated the production and provision of weather forecasts from international operational centers, from short range to medium range, to seasonal and decadal time scales. In June 2024, the WMO approved the inclusion of global climate reanalysis in their WMO Integrated Processing and Prediction System (WIPPS), which means it is now formally an operational activity within WIPPS. This will ensure that such vital datasets already produced operationally by participating centers are delivered to users in a unified, WIPPS-compliant framework, with regular traceable updates in a timely fashion. The lead center coordinating this activity will facilitate intercomparison of global reanalysis products from several centers, with the provision of comparable data on identical grids, and graphical products and visualization tools.

We demonstrate a data-driven approach that gives skilful annual predictions of province-scale maize yield across China's Northeast Farming Region (NFR), using the June-August (JJA) mean temperature and total precipitation as predictors. Our work builds on a method used to explore climate-related risks to maize production in the USA and South Africa. The approach uses a two-dimensional Gaussian function to parametrise the relationship between the detrended maize yields and summer climate conditions, which allows for non-linear growth responses to the individual climate variables. To enable skilful annual yield predictions we extend the approach in three ways: i) testing and validating out-of-sample yield and yield shock predictions; ii) introducing partial pooling of data across the provinces to allow for systematic differences between them, resulting in more robust model parameter estimation; iii) iterative correction of biases introduced by yield detrending procedures. Maximal yields occur for mean JJA temperature 21 degrees C-22 degrees C and total JJA rainfall 400 mm (corresponding to monthly rainfall totals of similar to 130 mm), which broadly agree with previous applications of similar approaches in different countries, giving confidence that the approach is robust despite inherent approximations. The model also demonstrates skilful retrospective forecasts of province area-average maize yield, giving out-of-sample Pearson correlations of similar to 0.6 (statistically significant at 99.9% level) between forecasts and observations for 1979-2016. Furthermore, including the August Standardised Precipitation-Evapotranspiration Index (SPEI) as an extra predictor improves out-of-sample predictions for low yield events (e.g. yields at least 10% below average) associated with adverse climate conditions, as occurred in Liaoning in 2000. The extended modelling framework can provide maize yield predictions using either weather observations or seasonal climate forecasts, and offers the potential for climate services that could help manage impacts on the food system due to adverse weather conditions.

The north China July precipitation (NCJP) accounts for the largest proportion of annual total precipitation in north China and displays a significant decadal variability of a cycle about 20-year at least from the past 40 years. There is a significant negative correlation between the July northern North Atlantic sea surface temperature (NNASST) and NCJP on decadal scale. When the detrended July NNASST anomaly is in negative phase, the NCJP anomaly is in positive phase, and vice versa. We present the coupled oceanic-atmospheric bridge (COAB) mechanism of the July NNASST influences on NCJP decadal variability via the Eurasian decadal teleconnection (EAT) pattern: the NNASST stimulates the EAT pattern which regulates circulation anomalies in north China and its surrounding regions, and finally regulates precipitation anomalies in north China by influencing the local water vapour transport. In addition, a preliminary analysis about the causes of the super-abnormal NCJP in 2021 indicates that the super-abnormal NCJP in 2021 is not just the decadal role of the NNASST on NCJP, may be the combined influence of the WPSH in the northeastern of north China, the abnormal high pressure in the northwest and the low-pressure cyclone in the south in the rainy decadal background.

The Madden-Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal-to-seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, considering the quasi-consecutive actives and eastward propagating features, a standard metric is proposed to identify MJO events based on the real-time multivariate MJO (RMM) index. The re-identification of historical MJO events reveals that there were 5.4 MJO events each year since 1981, and the average duration of each event is about 31.5 days. More MJO events tend to occur in the boreal winter and spring, with stronger intensity, longer duration and faster propagating speed than those in the boreal summer. Furthermore, MJO events are more likely to initiate in Phases 2 and 5, with a longer lifetime than those initiating in other phases. The amplitude and propagation characteristics of MJO events are strongly modulated by the dominant modes of sea surface temperature interannual variability with a strong regional dependence. Based on hindcast datasets of six S2S models, the prediction skill for the MJO is evaluated in the perspective of individual events. The longest leading time of the skilful prediction for individual MJO events ranges from 11 to 17 days, far below the traditional recognition. This result could be attributed to the apparent prediction barrier of MJO initiation, that is, a rapid decrease in prediction skill when predictions are carried out before the initiation of MJO events. In addition, the prediction skills of MJO events depends on interannual variabilities, with relatively higher skills under the conditions of the El Nino and Indian Ocean basin warming. These findings may shed light on the complexity and challenges of profoundly understanding and skilfully predicting MJO events.

Monthly precipitation over north China in August (NCAP) is the second highest in the year, and it is important to understand its driving mechanisms to facilitate reliable forecasting. The NCAP displays a significant decadal variability of a cycle about 10-year and negatively correlates with the July north-east North Atlantic Tripole (NAT) over the decadal timescales. This study shows that the Eurasian decadal teleconnection (EAT) acts as a bridge that links the July NAT with NCAP decadal variability. This coupled ocean—atmosphere bridge (COAB) mechanism, through which the July NAT influences the decadal variability of NCAP, can be summarized as follows. The cumulative effect of the NAT drives the EAT to adjust atmospheric circulation over north China and the surrounding regions, and so regulates precipitation in north China by influencing local water vapor transport. When the July NAT is in a negative (positive) phase, the EAT pattern has a positive (negative) pattern, which promotes (weakens) the transmission of water vapor from the sea in the south-east to north China, thus increasing (decreasing) NCAP over decadal timescales. The decadal NCAP model established based on the July NAT can effectively predict the NCAP decadal variability, illustrating that the July NAT can be implicated as a predictor of the NCAP decadal variability.

Skillful subseasonal prediction is crucial for meteorological disaster prevention and risk management. In this study, the subseasonal prediction skills of the new-generation coupled model of Beijing Climate Center (named as BCC-CSM2-HR) were evaluated, and a dynamical-statistical prediction model (DSPM) was developed to further improve pentad-mean precipitation predictions in China. The results show that although BCC-CSM2-HR can generally capture the climatological rain belt movement over eastern China, its skillful predictions for rainfall anomalies are basically confined within 3 pentads. By combining the dynamical model output and statistical method, a DSPM was built to capture the simultaneously coupled evolving patterns between anomalous precipitation and its atmospheric circulation predictors for each subregion of China, which was divided in terms of a cluster analysis. The 9-year independent validation shows that the prediction skills of DSPM had been significantly improved after 3 forecast pentads compared with the original model forecast. The skillful prediction can persist for a 6-pentad lead especially over the northern China and the Yangtze-Huaihe River Basin in the DSPM. As the major predictability sources of subseasonal forecasts, the Madden–Julian oscillation (MJO) and boreal summer intraseasonal oscillation (BSISO) are skillfully predicted by the BCC model for up to 23 days and 10–13 days, respectively. As a result, the improved performance of the DSPM can be largely attributed to its more realistic representation of MJO and BSISO associated circulation anomalies.

Since the meridional positions of the east and west part of western Pacific subtropical high（WPSH） affect the climate over China differently,we redefined the meridional index of WPSH in this study.The mean latitude of the location of 500 hPa geopotential height maximum along several meridian in 10°—60°N,110°—130°E and 10°—60°N,130°—150°E is defined as new west index（Index＿NEW＿west） and new east index（Index＿NEW＿east）, respectively.Their average value is defined as new meridional index of WPSH（Index＿NEW）.The correlation between these indices and summer precipitation over eastern China is more significant than the ridge index defined by National Climate Centre（Index＿NCC）,specifically for the positive correlation between Index＿NEW＿west and precipitation over North China Plain.The negative correlation between Index＿NEW＿east and precipitation over Yangtze River Basin is similar to that of Index＿NCC.Regression of the 500 hPa horizontal wind with respect to Index＿NEW＿west（Index＿NEW＿east） presents anticyclonic circulation over western North Pacific and the anticyclone center locates near 38°N,130°E（40°N, 145°E）.The various collocations between north/south anomalies of Index＿NEW＿west/Index＿NEW＿east correspond to four types of rainfall patterns.The good relationship between the interannual variation of new indices and summer precipitation over eastern China could provide more valuable references for the study of precipitation prediction and interseasonal movement of rainfall band.

We present results from the first 6 years of this major U.K. government funded project to accelerate and enhance collaborative research and development in climate science, forge a strong strategic partnership between U.K. and Chinese climate scientists, and demonstrate new climate services developed in partnership. The development of novel climate services is described in the context of new modeling and prediction capability, enhanced understanding of climate variability and change, and improved observational datasets. Selected highlights are presented from over 300 peer reviewed studies generated jointly by U.K. and Chinese scientists within this project. We illustrate new observational datasets for Asia and enhanced capability through training workshops on the attribution of climate extremes to anthropogenic forcing. Joint studies on the dynamics and predictability of climate have identified new opportunities for skillful predictions of important aspects of Chinese climate such as East Asian summer monsoon rainfall. In addition, the development of improved modeling capability has led to profound changes in model computer codes and climate model configurations, with demonstrable increases in performance. We also describe the successes and difficulties in bridging the gap between fundamental climate research and the development of novel real-time climate services. Participation of dozens of institutes through subprojects in this program, which is governed by the Met Office Hadley Centre, the China Meteorological Administration, and the Institute of Atmospheric Physics, is creating an important legacy for future collaboration in climate science and services.

The provision of climate services for assessing and governing environmental problems such as poor air quality requires interactions between scientists and decision-makers. Air quality information services in China mainly focus on the coming days to weeks. However, users may benefit from air quality information on climate time-scales—from months to decades; hereafter air quality climate services. We focused on key decision-makers and stakeholders that are users of air quality climate services and conducted five workshops with these identified users to ascertain their priorities for air quality climate services, and the reasoning behind these priorities. We also conducted a choice-based conjoint experiment via an online survey distributed amongst regional and local Climate Centres and Environmental Monitoring Centres to assess quantitatively the decision-makers' needs. The results from the workshops and the survey showed that the needs for air quality climate services by users in China mainly relate to seasonal forecasting of winter haze events (PM2.5 levels and/or the meteorological conditions conducive to the dispersion of the air pollution); there is also some interest in long-term projections of haze under climate change and a growing interest in ozone pollution in summer. Spatial relevance is perceived to be important to regional and city-level stakeholders who prefer information on the city-level, whilst national-wide information is important for national government agencies. A high level of reliability of forecasts was needed for uptake. The findings on the needs for air quality climate services by potential users can support researchers and policy-makers in developing the scientific capacity and providing tailored and effective air quality climate services in China.

This study uses the NCEP/NCAR monthly average reanalysis and number of haze day data during 1958-2017, and the average daily PM2.5 mass concentration data during 2013-2017, to calculate the East Asian winter monsoon index (EAWMI) and statistically analyze the correlation between the winter monsoon index and air quality in China, particularly for the five typical regions (Beijing-Tianjin-Hebei, Fen-Wei Plain, Sichuan-Chongqing Delta, Yangtze River Delta, and Pearl River Delta). Thereafter, the strong and weak winter monsoon years were classified based on the EAWMI, and the atmospheric circulation and temperature fields over China and the five regions in different winter monsoon years were spatially compared. Finally, the study also investigated the various distribution features of the climatic circulation background responsible for the strong and weak winter monsoons and their impact mechanisms on air quality the five typical regions in China. The results show that the effect of the winter monsoon on the air quality of China may be represented by a north-south boundary line located at approximately 30 degrees N. During strong winter monsoon years, pollution was lower in the area north of the boundary but higher to its south. By contrast, the opposite phenomenon was observed during the weak monsoon years. During the strong winter monsoon years, the Beijing-Tianjin-Hebei region and Fen-Wei Plain to the north of the boundary line were less polluted, while the Yangtze River, Chengdu-Chongqing, and Pearl River delta regions to the south of the boundary were more polluted. Diagnostic analysis of the circulation field indicated that during the strong winter monsoon years, an abnormal downward airflow occurred to the south of the boundary, limiting convective diffusion and thereby causing the increased pollution. However, during the weak winter monsoon years, ascending airflows occurred, which favored pollutant diffusion. Furthermore, during the strong winter monsoon years, an abnormal southeast airflow with weak horizontal wind speed occurred in the lower atmosphere of the Chengdu-Chongqing region, causing localized pollutant accumulation, thereby aggravating the pollution. In the Pearl River Delta region, a descending abnormal westerly flow inhibited the local uplift and diffusion of air. Moreover, importing pollutants occurred from the north, aggravating the pollution in the region.

Long-lead precipitation forecasts for 1-4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Nino event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Nino-related SST anomalies in the BCC_CSM1.1 m.

Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons.

本文研制建立了一个预测青海省夏季降水的动力—统计相结合的组合降尺度预测方法(Hybrid Statistical Downscaling Prediction,HSDP),该方法综合利用了 气候模式 Climate Forecast System 2.0版本(CFSv2)实时预测的高可预报性环流信息及前期观测的与青海夏季降水具有高相关性的气候因子,采用年际增量方法,基于气候变量的年际增量规律建立统计模型,从而实现对青海夏季降水进行动力—统计相结合的气候预测.根据全球气候因子的年际增量与青海省夏季降水年际增量的相关系数,以及CFSv2预测产品对实况模拟能力的评估,选取以下关键区气候变量的年际增量作为预测因子:(1)CFSv2模式预测当年夏季包含贝加尔湖脊、乌拉尔山脊和新疆脊区域的500 hPa高度场;(2)CFSv2模式预测青藏高原以西200 hPa纬向风场;(3)观测资料中前1 a秋、冬季热带太平洋地区海表面温度场;(4)观测资料中前1 a秋、冬季西伯利亚地区的海平面气压场,对青海省夏季降水进行统计降尺度预测.统计降尺度模型利用1983-2011年进行建模,回报2012-2018年夏季青海省降水的空间分布和时间变化,并对该模型对1983-2011年的夏季青海省降水的回报能力进行了交叉检验.回报结果表明该统计降尺度模型对CFSv2的青海省夏季降水预测能力有显著的提高,能够很好地再现青海省夏季降水西北部的高原地区偏少,而在东南部偏多的特点.该模型预测所得2012-2018年夏季青海省降水的时间变化也与实况有着较高的相关系数(0.76),对于降水显著偏少的年份(如2015年)和显著偏多的年份(如2012、2018年)的降水预测都有很好的表现.对于建模时段的交叉检验结果(相关系数为0.46,比模型回报结果与实况的相关系数0.48略低)表明,该模型具有较高的稳定性和可靠性.

Changes in climate pose major challenges to society, and so decision-makers need actionable climate information to inform their planning and policies to make society more resilient to climatic changes. Climate services are being developed to provide such actionable climate information. The successful development and use of climate services benefits greatly from close engagement between developers, providers, and users of the services. The Climate Science for Service Partnership China (CSSP China) is a China-UK collaboration fostering closer engagement between climate scientists, providers of climate services, and users of climate services. We describe the process within CSSP China of co-developing climate services through trials with users to revise and improve a prototype. Examples are provided covering various scientific capabilities, user needs, and parts of China. The development process is yielding many benefits, such as increasing the engagement between providers and users, making users more aware of how climate information can be of use in their decision-making, giving the climate service providers a better understanding of the users' requirements for climate information, and shaping future scientific research and development. In addition to the benefits, we also document some challenges that have emerged, along with ways of alleviating them. We have two key recommendations from our experiences: make the time and space for effective engagement between the users and developers of any climate service; bring the needs of the users in to the design and delivery of the climate service as early as possible and throughout the development cycle.

The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.

The Northeast Farming Region (NFR) is a major maize cropping region in China, which accounts for about 30% of national maize production. Although the regional maize production has an increasing trend in the last decades, it has greater inter-annual fluctuation. The fluctuation is caused by the increased variations of the local temperature and precipitation given the dominance of rainfed maize in the region. To secure high and stable level of maize production in the NFR under the warmer and drier future climate conditions, we employed a cross-scale model-coupling approach to identify the suitable maize cultivars and planting adaptation measures. Our simulation results show that, with proper adaptations of maize cultivars and adjustments of planting/harvest dates, both maize planting area and yield per unit of land will increase in most regions of NFR. This finding indicates that proactive adaptation can help local farmers to reap the benefits of increasing heat resource brought in by global warming, thus avoiding maize production losses as reported in other studies. This research can potentially contribute to the development of agricultural climate services to support climate-smart decisions for agricultural adaptations at the plot, farm and regional scales, in terms of planning the planting structure of multiple crops, breeding suitable maize varieties, and optimizing planting and field management schedules.