Previous studies have examined the projected climate types in China by 2100. This study identified the emergence time of climate shifts at a 1 • scale over China from 1990 to 2100 and investigated the temporal evolution of Köppen-Geiger climate classifications computed from CMIP5 multi-model outputs. Climate shifts were detected in transition regions (7%-8% of China's land area) by 2010, including rapid replacement of mixed forest (Dwb) by deciduous forest (Dwa) over Northeast China, strong shrinkage of alpine climate type (ET) on the Tibetan Plateau, weak northward expansion of subtropical winterdry climate (Cwa) over Southeast China, and contraction of oceanic climate (Cwb) in Southwest China. Under all future RCP (Representative Concentration Pathway) scenarios, the reduction of Dwb in Northeast China and ET on the Tibetan Plateau was projected to accelerate substantially during 2010-30, and half of the total area occupied by ET in 1990 was projected to be redistributed by 2040. Under the most severe scenario (RCP8.5), sub-polar continental winter dry climate over Northeast China would disappear by 2040-50, ET on the Tibetan Plateau would disappear by 2070, and the climate types in 35.9% and 50.8% of China's land area would change by 2050 and 2100, respectively. The results presented in this paper indicate imperative impacts of anthropogenic climate change on China's ecoregions in future decades.

The persistent extreme precipitation event (PEPE) that occurred over the Yangtze River Basin (YRB) during the period of 12–27 June 1998 is the most severe one in recent 60 years, and it is mainly caused by two significant components of intraseasonal oscillation (ISO) (10–30 days and 30–60 days) identified in this study. The two ISOs play different roles in the distributions of YRB rain belt in the PEPE; i.e., the 30–60 day ISO generally maintains the shape and intensity of YRB rain belt with its peak covering the whole PEPE period; however, the 10–30 day ISO mainly determines the south‐north swing of the YRB rain belt that features three PEPE stages. North Indian Ocean is the major forcing region of 30–60 day ISO, where anomalous warm sea surface temperature‐induced local strong convections stimulate a meridional teleconnection wave train over the East Asia, generating the 30–60 day intraseasonal YRB rainfalls. The 10–30 day ISO primarily originates from the northwest Pacific and the South China Sea (SCS), and along with its northwestward and northeastward propagations due to the air‐sea coupling and prevailing winds, suppressed and enhanced convections appear alternatively over the Philippine Sea and the central SCS in the three PEPE stages; thus, their stimulated downstream wave trains along the coast of East Asia vary accordingly in terms of phase and position, causing three stages of 10–30 day intraseasonal YRB rainfalls with different intensities and locations. These results suggest that proper combination of different intraseasonal oscillations is one of the essential and effective ways to produce the PEPEs.

In order to solve the problem of synchronous acquisition of multi-parameter data of photovoltaic special metering protection unit and reduce the noise pollution of sensor data, this paper designs a multi-parameter data acquisition system of photovoltaic special metering protection unit; The system uses RN7326 and SWM32F103R chips as the main and auxiliary microprocessors, and designs a scheme of multi-sensor signal filtering and interface resource acquisition; The standardized data communication protocol is studied and designed to realize the synchronous data acquisition of multiple sensors; Finally, the LMS (Least mean square) based on adaptive filtering method is used to reduce the noise of multi-parameter data. In this paper, the temperature sensor pt100 is taken as an example to conduct temperature acquisition and filtering processing experiments. After data filtering, the noise above 1Hz is basically eliminated, and the noise pollution is effectively reduced when the noise amplitude is less than 1Hz, and the accuracy of sensor data is improved.