We investigate historical and projected precipitation in Tanzania using observational and climate model data. Precipitation in Tanzania is highly variable in both space and time due to topographical variations, coastal influences, and the presence of lakes. Annual and seasonal precipitation trend analyses from 1961 to 2016 show maximum rainfall decline in Tanzania during the long rainy season in the fall (March–May), and an increasing precipitation trend in northwestern Tanzania during the short rainy season in the spring (September–November). Empirical orthogonal function (EOF) analysis applied to Tanzania’s precipitation patterns shows a stronger correlation with warmer temperatures in the western Indian Ocean than with the eastern-central Pacific Ocean. Years with decreasing precipitation in Tanzania appear to correspond with increasing sea surface temperatures (SST) in the Indian Ocean, suggesting that the Indian Ocean Dipole (IOD) may have a greater effect on rainfall variability in Tanzania than the El Niño-Southern Oscillation (ENSO) does. Overall, the climate model ensemble projects increasing precipitation trend in Tanzania that is opposite with the historical decrease in precipitation. This observed drying trend also contradicts a slightly increasing precipitation trend from climate models for the same historical time period, reflecting challenges faced by modern climate models in representing Tanzania’s precipitation.

This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.).

This paper explores the Madden–Julian Oscillation (MJO) modulation of tropical cyclone (TC; hereafter, MJO-TC) genesis over the South China Sea (SCS) and Western North Pacific (WNP) under different El Niño-Southern Oscillation (ENSO) conditions. Analyses used Joint Typhoon Warning Center (JTWC) best-track data, the Real-Time Multivariate MJO (RMM) index, and European Center for Medium-Range Weather Forecasts (ECMWF) Interim (ERA-Interim) reanalysis data. The results showed that the MJO has significant modulation on both the SCS and WNP TC genesis in neutral years, with more (fewer) TCs forming during the active (inactive) MJO phases. However, during the El Niño and La Niña years, the MJO-TC genesis modulation over the two regions differs from each other. Over the SCS, the MJO modulation of TC genesis is stronger in the La Niña years, while it becomes weaker in the El Niño years. Over the WNP, the MJO has a stronger influence on TC genesis in the El Niño years compared to that in the La Niña years. Related Genesis Potential Index (GPI) analysis suggests that midlevel moisture is the primary factor and vorticity is the secondary factor, for the MJO-TC genesis modulation over the SCS in the La Niña years. Over the WNP, midlevel moisture is the dominant factor for the MJO-TC genesis modulation during the El Niño years. These results can be explained by increased water vapor transport from the Bay of Bengal, associated with enhanced westerly wind anomalies, during the active phases relative to the inactive MJO phases; these conditions prevail over the SCS during the La Niña years, and over the WNP during the El Niño years.

During May and June (the Meiyu season) of 2017, Taiwan was affected by three heavy frontal rainfall events, which led to large economic losses. Using satellite observations and reanalysis data, this study investigates the impact of boreal summer intra-seasonal oscillations (BSISOs, including a 30–60 day ISO mode named BSISO1 and a 10–30 day ISO mode named BSISO2) on the heavy rainfall events in Taiwan during the 2017 Meiyu season. Our examinations show that BSISO2 is more important than BSISO1 in determining the formation of heavy rainfall events in Taiwan during the 2017 Meiyu season. The heavy rainfall events generally formed in Taiwan at phases 4–6 of BSISO2, when the enhanced southwesterly wind and moisture flux convergence center propagate northward into the Taiwan area. In addition, we examined the forecast rainfall data (at lead times of one day to 16 days) obtained from the National Centers for Environmental Prediction Global Forecast System (NCEPgfs) and the Taiwan Central Weather Bureau Global Forecast System (CWBgfs). Our results show that the better the model’s capability in forecasting the BSISO2 index is, the better the model’s capability in forecasting the timing of rainfall formation in Taiwan during the 2017 Meiyu season is. These findings highlight the importance of BSISO2 in affecting the rainfall characteristics in East Asia during the Meiyu season.

Water vapor transport plays a significant role in maintaining the water cycle over the Tibetan Plateau (TP). This study investigates the characteristics of water vapor transport across the TP southern boundaries and its impacts on TP precipitation during the Indian summer monsoon (ISM) season from 2000 to 2019. The southern boundary is subdivided into four sub-boundaries from the east to the west: boundaries 7 (100°–95° E), 8 (95°–89° E), 9 (89°–80° E), and 10 (80°–70° E) (B7, B8, B9, and B10). ISM can affect the water vapor transports of B7, B8, and B9, while mid-latitude westerlies dominate the water vapor transport of B10. An area with concentrated spatial precipitation in both strong and weak ISM months is regarded as a precipitation concentration region (PCR). The results show that the PCR precipitation is smaller in the weak ISM month than in the normal month for most of the day, while it is larger in the strong ISM month than in the normal month. The PCR precipitation difference from afternoon to evening in strong and weak ISM months between normal months shows a dependency on the water vapor transport of B8 and B10. Water vapor transported across B9 also contributes to the increased PCR precipitation in the afternoon. The PCR precipitation shows a more dependable relationship to the water vapor from the BoB and the Arabian Sea in strong ISM months than in weak ISM months. Conversely, the water vapor transport efficiency is low in strong ISM months due to a cyclonic circulation over northern India, preventing water vapor transport from reaching the TP directly.

The record-setting winter of 2022–2023 came as an answer to both figurative and literal prayers for political leaders, policy makers, and water managers reliant on snowpacks in the Upper Colorado River Basin, a vital source of water for tens of millions of people across the Western United States. But this “drought-busting” winter was not well-predicted, in part because while interannual patterns of tropical ocean temperatures have a well-known relationship to precipitation patterns across much of the American West, the Upper Colorado is part of a liminal region where these connections tend to be comparatively weak. Using historical sea surface temperature and snowpack records, and leveraging a long-term cross-basin relationship to extend the timeline for evaluation, this analysis demonstrates that the 2022–2023 winter did not present in accordance with other high-snowpack winters in this region, and that the associative pattern of surface temperatures in the tropical Pacific, and snow water equivalent in the regions that stored and supplied most of the water to the Colorado River during the 2022–2023 winter, was not substantially different from a historically incoherent arrangement of long-term correlation. These findings suggest that stochastic variability plays an outsized role in influencing water availability in this region, even in extreme years, reinforcing the importance of other trends to inform water policy and management.