Although the societal impact of a weather event increases with the rarity of the event, our current ability to assess extreme events and their impacts is limited by not only rarity but also by current model fidelity and a lack of understanding and capacity to model the underlying physical processes. This challenge is driving fresh approaches to assess highimpact weather and climate. Recent lessons learned in modeling high-impact weather and climate are presented using the case of tropical cyclones as an illustrative example. Through examples using the Nested Regional Climate Model to dynamically downscale large-scale climate data the need to treat bias in the driving data is illustrated. Domain size, location, and resolution are also shown to be critical and should be adequate to: include relevant regional climate physical processes; resolve key impact parameters; and accurately simulate the response to changes in external forcing. The notion of sufficient model resolution is introduced together with the added value in combining dynamical and statistical assessments to fill out the parent distribution of high-impact parameters.

This study examines how urbanization affects the precipitation climatology in Tokyo, Japan. A unique aspect of this study is that an ensemble, regional climatological simulation approach is used with sensitivity experiments to reduce uncertainty arising from nonlinearity in the precipitation simulations. Another aspect is that the robustness of the precipitation response is tested with ''stress response'' simulations with increasing urban forcing. The results show that urbanization causes a robust increase in the amount of precipitation in the Tokyo metropolitan area and a reduction in the inland areas. These anomalies are statistically significant at the 95% and 99% levels in some parts. There is no measureable change in the surrounding rural and ocean areas. These precipitation responses are attributed to an increase of surface sensible heat flux in Tokyo, which destabilizes the atmosphere and induces an anomalous surface low pressure pattern and the convergence of grid-scale horizontal moisture flux. The anomalous convergence of grid-scale horizontal moisture flux is a consequence of urbanization modifying the sea breeze.

This study evaluates the performance of a regional climate model in simulating two types of synoptic tropical weather disturbances: convectively-coupled Kelvin and easterly waves. Interest in these two wave modes stems from their potential predictability out to several weeks in advance, as well as a strong observed linkage between easterly waves and tropical cyclogenesis. The model is a recent version of the weather research and forecast (WRF) system with 36-km horizontal grid spacing and convection parameterized using a scheme that accounts for key convective triggering and inhibition processes. The domain spans the entire tropical belt between 45°S and 45°N with periodic boundary conditions in the east-west direction, and conditions at the meridional/lower boundaries specified based on observations. The simulation covers 6 years from 2000 to 2005, which is long enough to establish a statistical depiction of the waves through space-time spectral filtering of rainfall data, together with simple lagged-linear regression. Results show that both the horizontal phase speeds and three-dimensional structures of the waves are qualitatively well captured by the model in comparison to observations. However, significant biases in wave activity are seen, with generally overactive easterly waves and underactive Kelvin waves. Evidence is presented to suggest that these biases in wave activity (which are also correlated with biases in time-mean rainfall, as well as biases in the model's tropical cyclone climatology) stem in part from convection in the model coupling too strongly to rotational circulation anomalies. Nevertheless, the model is seen to do a reasonable job at capturing the genesis of tropical cyclones from easterly waves, with evidence for both wave accumulation and critical layer processes being importantly involved.

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per mean change in air temperature over Japan is found to be 2.4%/°C. Extreme precipitation intensity increases with temperatures up to 22 °C in future climate scenarios, while the peak is 20 °C for the current climate. Extreme precipitation intensities at higher percentiles are projected to have larger rates of increase (3-5%/°C in the current climate and 4-6%/°C in the future climate scenarios). A decrease of precipitation intensity at higher temperatures relates to water vapor availability. An insufficient water vapor supply for saturation at higher temperatures can lead to a decrease in cloud formation and extreme precipitation.

Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated based on hourly wet-bulb globe temperature (WBGT) obtained from the three-member ensemble multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical downscaling approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40% by the end of the twenty-first century. A 60-80% reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s.

Targeting to East Asian summer monsoon for the first time, this study presents an assessment of projection uncertainty in ensemble dynamical downscaling (DDS) simulations. Based on 12-member DDS simulations comprised of three global climate models (GCMs) and four regional climate models (RCMs), we evaluate contributions of GCM and RCM uncertainty to the total uncertainty of summertime precipitation projections around Japan. Our results show that contribution of RCM uncertainty can be comparable to that of GCM uncertainty in magnitudes. This finding draws a distinction from the past studies showing the dominance of GCM uncertainty. Most notably, our results show that RCM uncertainty for number of precipitating days appears around and over the land. RCM uncertainty for precipitation amounts also shows a dependence on topography but to a lessor degree. These RCM uncertainty characteristics are potentially linked to the difference in various RCM configurations such as physics schemes and model topography. In contrast, GCM uncertainty mostly appears over the ocean, which can be attributed to the difference in the GCM's future projections of East Asian summer monsoon. Our finding may be of an importance for water disaster and water resource management with DDS.