Skilful Forecasts of Summer Rainfall in the Yangtze River Basin from November

Variability in the East Asian summer monsoon(EASM)brings the risk of heavy flooding or drought to the Yangtze River basin,with potentially devastating impacts.Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers,supporting livelihoods and major economic and population centres across eastern China.This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November,and that this allows skilful forecasts of summer mean rainfall in the Yangtze River basin at a lead time of six months.The skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring,although the skill in June–July–August is much weaker and not consistently significant.However,there is some evidence for enhanced skill following El Niño events.The potential for decadal-scale variability in forecast skill is also examined,although we find no evidence for significant variation.

Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin

We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance.Much of the rainfall in this region during June is contributed by the mei-yu rain band.We find that similar skill exists for predicting the East Asian summer monsoon index(EASMI)on monthly time scales,and that the latter could be used as a proxy to predict the regional rainfall.However,there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly.Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations,which may be inherently unpredictable on the seasonal forecasting time scale,the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high.The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.

Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

Seasonal forecasts for Yangtze River basin rainfall in June,May–June–July(MJJ),and June–July–August(JJA)2020 are presented,based on the Met Office GloSea5 system.The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon(EASM)index,which is transformed into regional-mean rainfall through linear regression.The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation.The forecasts verify well in terms of giving strong,consistent predictions of above-average rainfall at lead times of at least three months.However,the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period,leading to observed values that lie outside the 95%prediction intervals of the three-month forecasts.The forecasts presented here are consistent with other studies of the 2020 EASM rainfall,whereby the enhanced mei-yu front in early summer is skillfully forecast,but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured.This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

Erratum to: Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin

The article[Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin],written by[Gill M.MARTIN,Nick J.DUNSTONE,Adam A.SCAIFE,and Philip E.BETT],was originally published electronically on the publisher’s internet portal on[10 December 2019]without open access.

Seasonal Rainfall Forecasts for the Yangtze River Basin of China in Summer 2019 from an Improved Climate Service

Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate service was developed in 2016, producing a prototype seasonal forecast product for use by stakeholders in the region, based on rainfall forecasts directly from a dynamical model. Here, we describe an improved service based on a simple statistical downscaling approach. Through using dynamical forecast of an East Asian summer monsoon(EASM) index, seasonal mean rainfall for the upper and middle/lower reaches of YRB can be forecast separately by use of the statistical downscaling, with significant skills for lead times of up to at least three months. The skill in different sub-basin regions of YRB varies with the target season. The rainfall forecast skill in the middle/lower reaches of YRB is significant in May–June–July(MJJ), and the forecast skill for rainfall in the upper reaches of YRB is significant in June–July–August(JJA). The mean rainfall for the basin as a whole can be skillfully forecast in both MJJ and JJA. The forecasts issued in 2019 gave good guidance for the enhanced rainfall in the MJJ period and the near-average conditions in JJA. Initial feedback from users in the basin suggests that the improved forecasts better meet their needs and will enable more robust decision-making.

Verification of the 2019 GloSea5 Seasonal Tropical Cyclone Landfall Forecast for East China

A prototype climate service was developed and trialled in early 2019 to provide seasonal forecast of the June–July–August(JJA) tropical cyclone(TC) landfall risk for the East China region ahead of the forthcoming typhoon season.Test forecasts were produced in both March and April 2019 and a final forecast was released to the China Meteorological Administration(CMA) on 1 May 2019. The trial service was produced by using the Met Office Global Seasonal forecast system(GloSea5), and a forecast of the western Pacific subtropical high(WPSH) index was used to infer the TC landfall risk based on a simple linear regression between historical model WPSH indices and observed TC landfalls in East China. The forecast method shows significant skill for forecasting the JJA TC landfall risk in East China with up to three-month lead time, with the greatest skill for predictions initialized in May. The 2019 forecast provided good guidance of the near-average TC activity observed in East China in JJA 2019. Success of the forecast adds confidence to an improved climate service ahead of the 2020 typhoon season.

The Process and Benefits of Developing Prototype Climate Services——Examples in China

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.