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Seasonal precipitation and temperature predictions with global climate models that are developed based on the ocean–atmosphere interactions, contribute to the water resources management and hazard mitigation. To date, multi-model ensemble seasonal climate prediction systems, such as North American multi-model ensemble (NMME), are an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean–atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, whether the NMME real-time forecasts perform as well as the hindcasts and how the decadal variability in ocean-atmospheric teleconnections affect the prediction skill remain unclear. In this paper, based on precipitation and temperature datasets from nine models of NMME, the evaluation of forecast skills during hindcast (1982–2010) and real-time forecast (2011–2020) periods is carried out in the Yangtze River basin over China. Results show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, but the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14–51% (38–75%) and error increased by 30–31% (51–55%) for seasonal precipitation (temperature) predictions up to the sixth lead-season, and the skill decrease is larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that a better grasp of decadal variability is needed to improve the real-time climate predictions. [ABSTRACT FROM AUTHOR]

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [ABSTRACT FROM AUTHOR]

Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs' average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models' estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs' efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively. [ABSTRACT FROM AUTHOR]