This study investigates precipitation variability over the Arabian Peninsula (AP) during its wet season. The wet season is split into winter (November – February) and spring (March and April) seasons, and early (1950–1986) and late (1986–2021) periods to understand sub-seasonal characteristics of precipitation variability and long-term changes in global teleconnections. The first three Empirical Orthogonal Functions explain ~70% of the interannual wet season precipitation variance, which shows an increase (decrease) in the late period winter (spring). Linear regression of the sea surface temperatures and geopotential height onto associated principal components reveals many oceanic and atmospheric variability patterns, which exhibit significant differences between winter and spring and early and late periods. Further, linear regressions of AP precipitation onto 14 natural modes of climate variability reveal a complex network of global teleconnections. El Niño-Southern Oscillation (ENSO) is one of the key contributors to precipitation variability but considering ENSO diversity is crucial to fully understand its influence. While the direct ENSO influence only becomes robust after the 1980s, its indirect effect persists through projection onto atmospheric modes, such as East Atlantic West Russia Pattern and East Atlantic Mode, or inter-basin interaction (e.g., via the Indian Ocean). The Northern Hemisphere atmospheric modes also mediate influences of other natural modes in tropical Indian and Atlantic oceans and extra-tropical regions over the AP. Several precipitation teleconnections exhibit a shift in the 1980s. Some may be related to the introduction of satellite data, but further investigations are warranted to understand the causes of these shifts.

Rainfed agriculture is the mainstay of economies across Southern Africa, where most precipitation is received during the austral summer monsoon. Despite that, seasonal precipitation predictability in Southern Africa is less explored. Here we use three natural climate forcings, El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD) – the dominant precipitation variability mode – to construct an empirical model that exhibits significant skill over Southern Africa during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over Southern Africa. This makes them less effective at predicting monsoon precipitation than the empirical model.

The monsoons in Pakistan have been exceptionally harsh in recent decades, resulting in extraordinary drought conditions and record flooding events. The changing frequency of extreme events is widely attributed to climate change. However, given this region's long history of floods and droughts, the role of natural climate variability cannot be rejected without a careful diagnosis. Here, we examine how oceanic and atmospheric variability has contributed to unusual precipitation distributions in West South Asia. Variations in sea surface temperatures in the tropical Pacific and northern Arabian Sea, and internal atmospheric variability related to the circumglobal teleconnection pattern and the subtropical westerly jet stream, explain more than 70% of monthly summer precipitation variability in the 21st century. Several of these forcings have co-occurred with record strength during episodes of extreme monsoons, which have exacerbated the overall effect. Climate change may have contributed to increased variability and the in-phase co-occurrences of the identified mechanisms, but further research is required to confirm any such connection.

Despite the necessity of Global Climate Models (GCMs) sub-selection in the dynamical downscaling experiments, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we relatively rank 37 GCMs from the 6th phase of Coupled Models Intercomparison Project (CMIP6) over four regions representing the contiguous United States (CONUS). The ranking is based on their performance across 60 evaluation metrics in the historical period (1981–2014). To ensure that the outcome is not method-dependent, we employ two distinct approaches to remove the redundancy in the evaluation criteria. The first approach is a simple weighted averaging technique. Each GCM is ranked based on its weighted average performance across evaluation measures, after each metric is weighted between zero and one depending on its uniqueness. The second approach applies empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. While the models from the same institute tend to display comparable skills, the high-resolution model versions distinctively perform better than their lower-resolution counterparts. The results from this study should be helpful in the selection of models for dynamical downscaling efforts, such as the COordinated Regional Downscaling Experiment (CORDEX), and in understanding the strengths and deficiencies of CMIP6 GCMs in the representation of various background climate characteristics across CONUS.