Climate change causing an increase of frequency and magnitude of heat waves has a huge impact on the urban population worldwide. In Indonesia, the Southeast Asian country in the tropical climate zone, the increasing heat wave duration due to climate change will be also magnified by projected rapid urbanization. Therefore, not only climate change mitigation measures but also adaptation solutions to more frequent extreme weather events are necessary. Adaptation is essential at local levels. The projected increase of the heat wave duration will trigger greater health-related risks. It will also drive higher energy demands, particularly in urban areas, for cooling. New smart solutions for growing urbanization for reducing urban heat island phenomenon are critical, but in order to identify them, analyzing the changing magnitude and spatial distribution of urban heat is essential. We projected the current and future spatial variability of heat stress index in three cities in Indonesia, namely, Medan, Surabaya, and Denpasar, under climate change and land-cover change scenarios, and quantified it with the Universal Thermal Climate Index (UTCI) for two periods, baseline (1981–2005) and future (2018–2042). Our results demonstrated that currently the higher level of the UTCI was identified in the urban centers of all three cities, indicating the contribution of urban heat island phenomenon to the higher UTCI. Under climate change scenarios, all three cities will experience increase of the heat, whereas applying the land-cover scenario demonstrated that in only Medan and Denpasar, the UTCI is likely to experience a higher increase by 3.1°C; however, in Surabaya, the UTCI will experience 0.84°C decrease in the period 2018–2042 due to urban greening. This study advanced the UTCI methodology by demonstrating its applicability for urban heat warning systems and for monitoring of the urban green cooling effect, as well as it provides a base for adaptation measures’ planning.

In Asia, where rice is a major crop, there is high concern about the detrimental effects of climate change on rice productivity. Evaluating these effects, considering the country-specific cultivars' responses to climate, is needed to effectively implement the national adaptation plans to maintain food security under climate change. However, to date, information on the effects of climate change on the local rice cultivars used in developing countries is extremely limited. In the present study, we used a process-based crop growth model, MATCRO-Rice, to predict the impact of climate change on yields of the major local rice cultivar Ciherang in Indonesia during the next 25 years 2018-2042 . This model simulated the effects of current to future air temperature, precipitation, and atmospheric carbon dioxide concentration on rice yield. A total of 14 future climate scenarios, derived from a combination of four general circulation models and three or four representative concentration pathway scenarios in the Coupled Model Intercomparison Project Phase 5, were used to consider the uncertainty of the future climate. The results showed that the rice yield was reduced under all climate scenarios, mainly because of the higher air temperature, leading to reduced photosynthetic rates, increased respiration rates, and phenological changes such as acceleration of senescence. The mean yield reduction across the 14 future climate scenarios was 12.1 for all of Indonesia in 2039-2042. Therefore, to maintain yields in Indonesia, rice production needs to adapt to climate change, and especially to higher air temperatures, in the near future.

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The bias correction of the General Circulation Model (GCM) outputs has become a routine step that is taken in climate change impact assessments. To responsibly support the decision‐making processes, the climate‐modeling community has been debating about the conceptual requirements that bias‐correction methods should fulfill. Bearing in mind these requirements, we propose to decompose atmospheric variables into three temporal elements that represent the climate mean state, the interannual variability, and the daily variability. This decomposition is aimed at correcting the biases at one time scale without affecting the simulated climate (mean state) trend or the distributional properties at other time scales. The novelty of the proposed approach is, nevertheless, marked by the adjustment of interannual and daily variability that is made by replacing the GCM‐simulated data with synthetic samples drawn from Stable Distributions (SDs) that are fitted to the observed variability. The replacement prevents the transfer of the sampling variability of the calibration period and gives the corrected data the distributional properties of the observed climate. The employment of SDs was motivated by the fact that the climate‐change‐induced changes in the scale, the symmetry, and the frequency of extremes can be measured and applied to the SDs of the observed data. We correct the biases in the GCM‐simulated temperature and precipitation over northern South America using our proposed approach and two other existing ones. Our proposed method is capable of not only preserving the simulated climate trends but also reproducing the observed extremes as well as a more flexible method based on nonparametric distributions.

<p>The post-processing of the Earth System Models (ESMs) outputs has become a routine step that is taken in climate change impact assessments with the aim of (i) reproducing the probability distribution of the corresponding observed data and (ii) correcting the biases in the probability distributions of projected future climate. To responsibly support the decision‐making processes, the climate‐modeling community has been discussing about the conceptual requirements that bias‐correction methods should fulfill to avoid altering the relevant information that is provided by ESMs, like the climate trends or the inter-variable physical dependence structure. Bearing in mind these discussions, a recently proposed method of bias-correction, based on TRend-preserving Synthetic Samples of Stable Distributions (TR3S), decomposes the atmospheric variables into three temporal elements that represent the climate mean state, the interannual variability, and the daily variability. This decomposition is aimed at correcting the biases at one time scale without affecting the projected climate trend or the distributional properties at other time scales. The novelty of this approach is, nevertheless, marked by the adjustment of interannual and daily variability that is made by replacing the ESM‐simulated variability with synthetic samples drawn from Stable Distributions (SDs) that were previously fitted to the observed variability. The replacement prevents the transfer of the sampling variability of the calibration period while giving the corrected data the distributional properties of the observed climate. The employment of SDs was motivated by the fact that the ESM-projected changes in the scale, the symmetry, and the frequency of extremes can be measured and applied to the SDs of the observed data. In this work, we correct the biases in the global precipitation datasets generated by several ESMs using the TR3S method and present the projected changes of a few indices of extremes using online interactive maps. Furthermore, the TR3S method allowed us to document the spatial distribution of the biases in the distributional properties (i.e., scale, symmetry, and frequency of extremes) of daily and interannual variability of each ESM. We hope that the bias-corrected information can be useful to end-users in impact assessments and the analytical framework of model biases can be used by modelers to identify ways in which the ESM parameterizations could be improved.</p>