Tropical cyclones (TCs) contribute to hydroclimates. The relationship between TC-related rainfall and TC activity was investigated using a high-resolution global atmospheric model. To separate the influences of the interannual variability of sea surface temperature (SST), a 64-member ensemble simulation was conducted for 11 TC seasons. TC direct rainfall (TCDR) and indirect rainfall (TCIR), within and away from the 500-kilometer distance from the TC center, were separately examined.

 The results show that TCDR was strongly correlated with TC activity, while TCIR was moderately correlated with TC activity. When TCs were more active, TCDR increased by more than twice in the southwestern region of Japan, and TCIR increased by up to 20% in the western part of Japan and the Pacific coastline of eastern Japan.

 A simple regression analysis showed that the relationship between seasonal TC activity and TC-related rainfall was independent of the interannual variability of the SST for TCDR but dependent on TCIR in the analysis area. The independent relationship between TC activity and TCDR likely becomes a useful metric for intermodel comparison and evaluation of the impact of global warming.

Super-resolution (SR) in deep learning is a technique to generate high-resolution (HR) outputs from low-resolution (LR) inputs. Recently, combining SR with data assimilation (DA) has been proposed, leading to the development of super-resolution data assimilation (SRDA). The SRDA method simultaneously performs SR and DA by inputting LR simulation results and observations into a neural network. This study develops a four-dimensional SRDA (4D-SRDA) model to predict temporal evolutions of three-dimensional quasi-geostrophic flows in a baroclinic jet system. To evaluate the performance of 4D-SRDA, we compare it with a Local Ensemble Transform Kalman Filter (LETKF), which uses an HR model. 4D-SRDA successfully reproduces both small- and large-scale structures of potential vorticity, visually similar to those produced by the LETKF. We compare grid-wise and pattern-similarity errors to quantify the accuracy of the analysis and forecast states. Despite using an LR fluid model, 4D-SRDA achieves accuracy comparable to that of the LETKF. Comparing the computational time required for prediction reveals that 4D-SRDA is substantially more efficient than the LETKF. These results suggest that 4D-SRDA is a promising approach for predicting HR atmospheric flows.