A total lightning data assimilation (LDA) scheme is developed at the cloud‐resolving scale in this study. The LDA scheme assimilates total lightning data through the ensemble square root filter (EnSRF) method based on the relationship between the maximum vertical velocity and the instantaneous flash rate. To verify the effect of LDA, three assimilation experiments based on a convective activity on July 6, 2019 are performed. The results show that all LDA experiments can improve the forecast. LDA improves the water vapor field and provides a warm and moist environment in which convection can develop. LDA can also increase the convergence at low levels and the divergence at upper levels, which leads to intense convection development. However, the LDA experiment that does not consider vertical localization (LDA_no) shows excessive adjustments of the model variables, which leads to overestimation of precipitation. The LDA experiment which adjusts the state variables in the charge zone (LDA_CZ) underestimates the forecast. The LDA experiment using a global group filter (GGF) localization function (LDA_GGF) is optimal in theory and produces a better forecast. The results of LDA_GGF show that the interaction between the cold pool and the strong low‐level vertical wind shear in front of the cold pool is beneficial for triggering new convection in front of the convection, which leads to a rapid shifting in convective activity. In addition, the LDA_GGF scheme is evaluated further for a rainfall process on August 1, 2020 and obtains similar forecast improvements for composite reflectivity and accumulated precipitation.

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The evolution of lightning generation and extinction is a nonlinear and complex process, and the nowcasting results based on extrapolation and numerical models largely differ from the real situation. In this study, a multiple-input and multiple-output lightning nowcasting model, namely Convolutional Long- and Short-Term Memory Lightning Forecast Net (CLSTM-LFN), is constructed to improve the lightning nowcasting results from 0 to 3 h based on video prediction methods in deep learning. The input variables to CLSTM-LFN include historical lightning occurrence frequency and physical variables significantly related to lightning occurrence from numerical model products, which are merged with each other to provide effective information for lightning nowcasting in time and space. The results of batch forecasting tests show that CLSTM-LFN can achieve effective forecasts of 0 to 3 h lightning occurrence areas, and the nowcasting results are better than those of the traditional lightning parameterization scheme and only inputting a single data source. After analyzing the importance of input variables, the results show that the role of numerical model products increases significantly with increasing forecast time, and the relative importance of convective available potential energy is significantly larger than that of other physical variables.

Model-based sensitivity experiments are a widely used method for studying climate change attributions. In traditional climate sensitivity studies, "climate drift" occurs because of the accumulation of model errors during long-term integrations, both in "actual" and "hypothetical" states. A more accurate piecewise-integration method is used to reduce the model errors, dividing the continuous simulation into a series of sequential short-term simulations. The model fields are updated at the end of each subinterval with analysis data for the "actual" state run, and with the sum of the analysis data and the difference between "hypothetical" and "actual" states for the "hypothetical" state run. This paper conducts sensitivity experiments with the continuous-integration method and the piecewise-integration method to evaluate the impacts of the central and eastern Pacific Ocean sea surface temperature anomalies (SSTAs) on the severe drought that occurred in southwestern China (SWC) in the winter of 2009/2010. The results show the following. (a) Model errors produced by the piecewise-integration method for the actual state simulation are significantly less than those produced by the continuous-integration method. (b) The intensity of the drought is significantly overestimated in continuous-integration experiments. However, the drought can be accurately simulated in its spatial distribution and intensity via the piecewise-integration method. Thus, climate sensitivity to changes in external forcing can be studied with greater credibility. (c) The warm central Pacific Ocean SSTAs may be the main cause of the drought over SWC. Adequate precipitation occurred over SWC when the centre of the SSTAs shifted to the east. (d) Warm SSTAs over the central Pacific Ocean influenced precipitation over SWC by weakening the water vapour transport branch from South China Sea to SWC. With a deficit of water vapour, the pronounced subsidence and warm temperatures were the main dynamic and thermodynamic factors that caused and maintained the drought. K E Y W O R D S climate change attribution, CP El Niño, drought over southwestern China, piecewise-integration method, sea surface temperature anomalies